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Is academic integrity research presented from a positive integrity standpoint? This paper uses Natural Language Processing (NLP) techniques to explore a data set of 8,507 academic integrity papers published between 1904 and 2019. Two main techniques are used to linguistically examine paper titles: (1) bigram (word pair) analysis and (2) sentiment analysis. The analysis sees the three main bigrams used in paper titles as being "academic integrity" (2.38%), "academic dishonesty" (2.06%) and "plagiarism detection" (1.05%). When only highly cited papers are considered, negative integrity bigrams dominate positive integrity bigrams. For example, the 100 most cited academic integrity papers of all time are three times more likely to have "academic dishonesty" included in their titles than "academic integrity". Similarly, sentiment analysis sees negative sentiment outperforming positive sentiment in the most cited papers. The history of academic integrity research is seen to place the field at a disadvantage due to negative portrayals of integrity. Despite this, analysis shows that change towards positive integrity is possible. The titles of papers by the ten most prolific academic integrity researchers are found to use positive terminology in more cases that not. This suggests an approach for emerging academic integrity researchers to model themselves after. [ABSTRACT FROM AUTHOR]

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]

The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks. [ABSTRACT FROM AUTHOR]

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work. [ABSTRACT FROM AUTHOR]

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users' expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6–7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, many immigrants found themselves in extremely unstable situations. The recent contributions show that employment decline in the first several months of the lockdown was higher for migrant workers than for natives. At the same time, migrants were less likely to find new employment in the recovery months. Such circumstances may result in an increased level of anxiety about one's economic situation. On the other hand, an unfavorable environment may induce resources that could help to overcome it. The paper aims to reveal migrants' concerns together with ambitions connected with the economic activity during the pandemic. The study is based on 30 individual in-depth interviews with Ukrainian migrant workers from Poland. The research approach was based on Natural Language Processing techniques. We employed sentiment analysis algorithms, and on a basis of selected lexicons, we extracted fears and hopes that appear in migrants' narrations. We also identified major topics and associated them with specific sentiments. Pandemic induced several matters connected with e.g., the stability of employment, discrimination, relationships, family, and financial situation. These affairs are usually connected on the basis of a cause-and-effect relationship. In addition, while several topics were common for both male and female participants, some of them were specific for each group. [ABSTRACT FROM AUTHOR]

Mobile apps for healthcare (mHealth apps for short) have been increasingly adapted to help users manage their health or to get healthcare services. User feedback analysis is a pertinent method that can be used to improve the quality of mHealth apps. The objective of this paper is to use supervised machine learning algorithms to evaluate the quality of mHealth apps according to the ISO/IEC 25010 quality model based on user feedback. For this purpose, a total of 1682 user reviews have been collected from 86 mHealth apps provided by Google Play Store. Those reviews have been classified initially into the ISO/IEC 25010 eight quality characteristics, and further into Negative, Positive, and Neutral opinions. This analysis has been performed using machine learning and natural language processing techniques. The best performances were provided by the Stochastic Gradient Descent (SGD) classifier with an accuracy of 82.00% in classifying user reviews according to the ISO/IEC 25010 quality characteristics. Moreover, Support Vector Machine (SVM) classified the collected user reviews into Negative, Positive, and Neutral with an accuracy of 90.50%. Finally, for each quality characteristic, we classified the collected reviews according to the sentiment polarity. The best performance results were obtained for the Usability, Security, and Compatibility quality characteristics using SGD classifier with an accuracy equal to 98.00%, 97.50%, and 96.00%, respectively. The results of this paper will be effective to assist developers in improving the quality of mHealth apps. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions. [ABSTRACT FROM AUTHOR]

This paper analyses Twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine learning techniques, to classify specific keywords. The proposed methodology initially performs data pre-processing, followed by multi-aspect sentiment analysis for computing the sentiment score of the extracted keywords, for precisely classifying users into various clusters based on similarity score with respect to a sample user in each cluster. The proposed technique also predicts the sentiment of a profile towards unknown keywords and gauges the bias of an unidentified user towards political events or social issues. The proposed technique was tested on Twitter dataset with 1.72 million tweets taken from over 10,000 profiles and was able to successfully identify the political leniency of the user profiles with 99% confidence level, and also on a synthetic dataset with 2500 tweets, where the predicted accuracy and F1 score were 0.99 and 0.985 respectively, and 0.97 and 0.975 when neutral users were also considered for classification. The paper could also identify the impact of political decisions on various clusters, by analyzing the shift in the number of users belonging to the different clusters. [ABSTRACT FROM AUTHOR]

Aspect-base sentiment analysis (ABSA) refers to the analysis of sentiments in users' notes from the perspective of different aspects, which has attracted much attention in the last decade. With the development of social networks, user-generated content on various Internet platforms is growing rapidly. This content contains valuable information, especially in e-commerce, which helps people and consumers make better decisions. Therefore, improving the performance of ABSA methods has recently attracted the attention of many researchers. In this paper, the basic concepts in sentiment analysis, applications, challenges, evaluation metrics, common benchmark datasets, required pre-processing and textual representation methods have been defined. An overview of all available methods for ABSA is also provided, and then a detailed review of deep learning methods in ABSA with a focus on graph neural networks has been done. Finally, this paper reviews the latest studies that have used deep learning and graph neural networks to solve sentiment analysis problems. We hope that our summaries in this paper can provide necessary guidelines for beginners and new researchers. [ABSTRACT FROM AUTHOR]

Sentiment analysis involves extracting sentiments from various forms of text, including customer reviews, tweets, blogs, and news clips expressing opinions on diverse subjects, even populist events. The advent of tools supporting regional languages has resulted in a substantial surge of regional language texts. As Hindi ranks fourth in terms of native speakers, the development of sentiment analysis mechanisms for Hindi text becomes crucial. This paper provides a comprehensive review of specific approaches used in Hindi sentiment analysis, encompassing negation handling and the evolution of SentiWordNet for the Hindi Language. Moreover, it offers an overview of available Hindi lexicons and insights into diverse stemmers and morphological analyzers designed for the language. Additionally, the paper conducts an in-depth literature review of various sentiment analysis tasks carried out in Hindi, thereby opening avenues for future research in sentiment analysis and opinion mining in the Hindi language. [ABSTRACT FROM AUTHOR]

Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people's hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people's sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people's sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people's reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people's feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers. [ABSTRACT FROM AUTHOR]

The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance. [ABSTRACT FROM AUTHOR]

Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should accurately identify flat and hierarchical topics, especially useful in disciplines where topics can be logically arranged into a tree format. In this paper, we propose Community Topic, a novel algorithm that exploits word co-occurrence networks to mine communities and produces topics. We also evaluate the proposed approach using several metrics and compare it with usual baselines, confirming its good performances. Community Topic enables quick identification of flat topics and topic hierarchy, facilitating the on-demand exploration of sub- and super-topics. It also obtains good results on datasets in different languages. [ABSTRACT FROM AUTHOR]

In most cases, the valuation of the investments characterized by various stages with a high level of uncertainty is done through the compound real option valuation (ROV). This decision making support can consider various types of uncertainty that can affects these investment phases, such as that linked to technology. Specifically, within the category of uncertain investments there are the broadband opportunities that can be valued as real options in order to quantify the risks associated with the investment. However, since ROV theory has no definitive way to determine model parameters based on market information, we propose one that can adjust them dynamically. In this paper, to include this aspect in the project valuation, we have unified the ROV with the sentiment analysis, a natural language processing technique that allows us to quantify the polarity of expressions in natural language numerically. In particular, the inherent risks related to the different phases of the project can be extracted from the information present in the surrounding environment and published in newspapers. From there, we obtain a sentiment score which, through appropriate manipulations, manages to modify the evaluation of the success probabilities of each stage. Then, we embed these success probabilities in the ROV in order to provide a valuation methodology that includes the impact of information on the investment decision. [ABSTRACT FROM AUTHOR]

Sentiment analysis is an important natural language processing (NLP) task due to a wide range of applications. Most existing sentiment analysis techniques are limited to the analysis carried out at the aggregate level, merely providing negative, neutral and positive sentiments. The latest deep learning-based methods have been leveraged to provide more than three sentiment classes. However, such learning-based methods are still black-box-based methods rather than explainable language processing methods. To address this gap, this paper proposes a new explainable fine-grained multi-class sentiment analysis method, namely MiMuSA, which mimics the human language understanding processes. The proposed method involves a multi-level modular structure designed to mimic human's language understanding processes, e.g., ambivalence handling process, sentiment strength handling process, etc. Specifically, multiple knowledge bases including Basic Knowledge Base, Negation and Special Knowledge Base, Sarcasm Rule and Adversative Knowledge Base, and Sentiment Strength Knowledge Base are built to support the sentiment understanding process. Compared with other multi-class sentiment analysis methods, this method not only identifies positive or negative sentiments, but can also understand fine-grained multi-class sentiments, such as the degree of positivity (e.g., strongly positive or slightly positive) and the degree of negativity (e.g., slightly negative or strongly negative) of the sentiments involved. The experimental results demonstrate that the proposed MiMuSA outperforms other existing multi-class sentiment analysis methods in terms of accuracy and F1-Score. [ABSTRACT FROM AUTHOR]

Over the last decade, deep learning has revolutionized machine learning. Neural network architectures have become the method of choice for many different applications; in this paper, we survey the applications of deep learning to natural language processing (NLP) problems. We begin by briefly reviewing the basic notions and major architectures of deep learning, including some recent advances that are especially important for NLP. Then we survey distributed representations of words, showing both how word embeddings can be extended to sentences and paragraphs and how words can be broken down further in character-level models. Finally, the main part of the survey deals with various deep architectures that have either arisen specifically for NLP tasks or have become a method of choice for them; the tasks include sentiment analysis, dependency parsing, machine translation, dialog and conversational models, question answering, and other applications. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is the computational analysis of the ideas, feelings, and opinions that determines the polarity of the text documents or comments using natural language processing (NLP) and text analyses techniques. The purpose of the multi-domain SA is to train a classifier using an appropriate set of tagged data to reduce the need for large amounts of data on specific domains and to address their data scarcity challenges using existing data in other domains. A combined use of the pre-trained BERT model, convolutional neural network (CNN), bi-directional long short-term memory (LSTM) and gated recurrent unit (GRU) is exploited in the proposed method of this paper for analysing the multi-domain sentiments using capsule network (CapsuleNet). In the proposed model of this paper, the pre-trained BERT (with CNN) and LSTM extracts the proper features for the CapsuleNet. The proposed approach is evaluated using the Dranziera protocol and the experimental results show that the accuracy of the proposed method is improved in comparison with the other basic deep learning-based methods, such as Multi CNN and LSTM. The results of the experiments show the superiority of the proposed method compared to the other similar methods on in-domain and out-of-domain data. [ABSTRACT FROM AUTHOR]

Deep learning models have showcased remarkable performances in natural language processing tasks. While much attention has been paid to improvements in utility, privacy leakage and social bias are two major concerns arising in trained models. In this paper, we address both privacy protection and gender bias mitigation in classification models simultaneously. We first introduce a selective privacy-preserving method that obscures individuals' sensitive information by adding noise to word embeddings. Then, we propose a negative multi-task learning framework to mitigate gender bias, which involves a main task and a gender prediction task. The main task employs a positive loss constraint for utility assurance, while the gender prediction task utilizes a negative loss constraint to remove gender-specific features. We have analyzed four existing word embeddings and evaluated them for sentiment analysis and medical text classification tasks within the proposed negative multi-task learning framework. For instances, RoBERTa achieves the best performance with an average accuracy of 95% for both negative and positive sentiment, with 1.1 disparity score and 1.6 disparity score respectively, and GloVe achieves the best average accuracy of 96.42% with a 0.28 disparity score for the medical task. Our experimental results indicate that our negative multi-task learning framework can effectively mitigate gender bias while maintaining model utility for both sentiment analysis and medical text classification. [ABSTRACT FROM AUTHOR]

In the field of natural language processing, sentiment analysis via deep learning has a excellent performance by using large labeled datasets. Meanwhile, labeled data are insufficient in many sentiment analysis tasks, and obtaining these data is time-consuming and laborious. Prompt learning devotes to resolving the data deficiency by reformulating downstream tasks with the help of prompt. The model performance of this method depends on the quality of the prompt. This paper proposes an adaptive prompting (AP) construction strategy using seq2seq-attention structure to acquire the semantic information of the input sequence. Our method of dynamically constructing adaptive prompts can not only improve the quality of prompt, but also can effectively generalize to other fields by constructing a pre-trained prompt with existing public labeled data. The experimental results on FewCLUE datasets demonstrate that the proposed method AP can effectively construct appropriate adaptive prompt regardless of the quality of hand-crafted prompt and outperform the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Short text information has considerable commercial value and immeasurable social value. Natural language processing and short text sentiment analysis technology can organize and analyze short text information on the Internet. Natural language processing tasks such as sentiment classification have achieved satisfactory performance under a supervised learning framework. However, traditional supervised learning relies on large-scale and high-quality manual labels and obtaining high-quality label data costs a lot. Therefore, the strong dependence on label data hinders the application of the deep learning model to a large extent, which is the bottleneck of supervised learning. At the same time, short text datasets such as product reviews have an imbalance in the distribution of data samples. To solve the above problems, this paper proposes a method to predict label data according to semi-supervised learning mode and implements the MixMatchNL data enhancement method. Meanwhile, the Bert pre-training model is updated. The cross-entropy loss function in the model is improved to the Focal Loss function to alleviate the data imbalance in short text datasets. Experimental results based on public datasets indicate the proposed model has improved the accuracy of short text sentiment recognition compared with the previous update and other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Financial sentiment analysis is a very challenging problem because the market is influenced by various factors, such as company-specific/political news, sentiment/opinions of users, and other regional financial market. Good news can drive the market to grow positively, while negative news can drag the market downwards. For this reason, it is crucial to understand the impacts of news and social media on the stock market trends. Motivated by this, this paper focuses on developing an effective and efficient company-specific financial sentiment analysis model which can detect the trends of a company's stock price. More specifically, we develop a novel neural network model that transforms pretrained general word embeddings into domain-specific embeddings. In addition, we use a knowledge-base to enrich the training vocabulary, and thus extend the domain-specific embedding space. The main challenge for natural language processing (NLP) applications is to learn the representation for the rare and unseen words. Another challenge for financial sentiment analysis models addressed in this paper is to deal with words that change their polarities depending upon the domain in which they are used. We thoroughly evaluate the performance of the proposed model on a benchmark dataset of SemEval-2017 shared task on financial sentiment analysis. The experimental results show that the proposed model delivers state-of-the-art performance when applied on Twitter and news headlines datasets, thus demonstrating its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Sentiment analysis has become one of the most active research areas in natural language processing, and the Arabic language retains its importance in this field. It is so because of the increased use of Arabic on the internet that pushes many users to share their views or thoughts about certain products and services. Despite its crucial importance, most of the existing Arabic sentiment analysis studies have been performed on document or sentence levels with little attention to the aspect level. However, the aspect level's main objective, also known as aspect-based sentiment analysis, is to extract the discussed aspects and identify their related sentiment polarities from a given review or text. The result is to provide more detailed information than general sentiment analysis. Therefore, this paper seeks to provide a comprehensive review of the Arabic aspect-based sentiment analysis studies and highlights the main challenges that face the different proposed approaches. The relevant gaps in the current literature and the future research directions in this area are also discussed. This survey can guide future researchers who want to contribute to the improvement of this domain. [ABSTRACT FROM AUTHOR]

We cannot overemphasize the essence of contextual information in most natural language processing (NLP) applications. The extraction of context yields significant improvements in many NLP tasks, including emotion recognition from texts. The paper discusses transformer-based models for NLP tasks. It highlights the pros and cons of the identified models. The models discussed include the Generative Pre-training (GPT) and its variants, Transformer-XL, Cross-lingual Language Models (XLM), and the Bidirectional Encoder Representations from Transformers (BERT). Considering BERT's strength and popularity in text-based emotion detection, the paper discusses recent works in which researchers proposed various BERT-based models. The survey presents its contributions, results, limitations, and datasets used. We have also provided future research directions to encourage research in text-based emotion detection using these models. [ABSTRACT FROM AUTHOR]

The Part-Of-Speech tagging is widely used in the natural language process. There are many statistical approaches in this area. The most popular one is Hidden Markov Model. In this paper, an alternative approach, linear-chain Conditional Random Fields, is introduced. The Conditional Random Fields is a factor graph approach that can naturally incorporate arbitrary, non-independent features of the input without conditional independence among the features or distributional assumptions of inputs. This paper applied the Conditional Random Fields for the car review word Part-Of-Speech tagging and then the feature extraction, which can be used as an input to an opinion mining system. To reduce the computational time, we also proposed applying the Limited-memory BFGS algorithm to train the Conditional Random Fields. Furthermore, this paper evaluated the Conditional Random Fields and the classical graph approach using the car review dataset to demonstrate that the Conditional Random Fields have a more robust result with a smaller training dataset. [ABSTRACT FROM AUTHOR]

The Covid pandemic has become a serious public health challenge for people across India and other nations. Nowadays, people rely on the online reviews being shared on different review sites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, since these reviews are large in number and are unstructured, patients struggle to get accurate and reliable information about the hospitals, due to which they end up taking admission into a hospital which might not be appropriate for the specific treatment they require. This paper employs the use of sentiment analysis to understand various online reviews of hospitals and provide valuable information to the patients. Approximately 30,000 + reviews were collected from more than 500 hospitals. The broad objective of the study is to give the patients a comprehensive and descriptive rating of the hospitals based on the online reviews given by different patients. In addition to providing a comprehensive summary, the study has conducted aspect-based analysis where it compares the hospitals based on four different aspects of the hospital viz. "Doctors' services", "Staff's services", "Hospital facilities", and "Affordability". The database containing aspect-based ratings of the hospitals will be of great value to the patients by allowing them to compare and select the best hospital based on the optimum fit of the aspects of their preference. [ABSTRACT FROM AUTHOR]

With the increasing popularity of open-source platforms, software data is easily available from various open-source tools like GitHub, CVS, SVN, etc. More than 80 percent of the data present in them is unstructured. Mining data from these repositories helps project managers, developers and businesses, in getting interesting insights. Most of the software artefacts present in these repositories are in the natural language form, which makes natural language processing (NLP) an important part of mining to get the useful results. The paper reviews the application of NLP techniques in the field of Mining Software Repositories (MSR). The paper mainly focuses on sentiment analysis, summarization, traceability, norms mining and mobile analytics. The paper presents the major NLP works performed in this area by surveying the research papers from 2000 to 2018. The paper firstly describes the major artefacts present in the software repositories where the NLP techniques have been applied. Next, the paper presents some popular open-source NLP tools that have been used to perform NLP tasks. Later the paper discusses, in brief, the research state of NLP in MSR field. The paper also lists down the various challenges along with the pointers for future work in this field of research and finally the conclusion. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze the Arab peoples reactions and attitudes towards the Russo-Ukraine War through the social media of posted tweets, as a fast means to express opinions. We scrapped over 3 million tweets using some keywords that are related to the war and performed sentiment, emotion, and partiality analyses. For sentiment analysis, we employed a voting technique of several pre-trained Arabic language foundational models. For emotion analysis, we utilized a pre-constructed emotion lexicon. The partiality is analyzed through classifying tweets as being 'Pro-Russia', 'Pro-Ukraine', or 'Neither'; and it indicates the bias or empathy towards either of the conflicting parties. This was achieved by constructing a weighted lexicon of n-grams related to either side. We found that the majority of the tweets carried 'Negative' sentiment. Emotions were not that obvious with a lot of tweets carrying 'Mixed Feelings'. The more decisive tweets conveyed either 'Joy' or 'Anger' emotions. This may be attributed to celebrating victory ('Joy') or complaining from destruction ('Anger'). Finally, for partiality analysis, the amount of tweets classified as being 'Pro-Ukraine' was slightly greater than Pro-Russia' at the beginning of the war (specifically from Feb 2022 till April 2022) then slowly began to decrease until they nearly converged at the start of June 2022 with a shift happening in the empathy towards Russia in August 2022. Our Interpretation for that is with the initial Russian fierce and surprise attack at the beginning and the amount of refugees who escaped to neighboring countries, Ukraine gained much empathy. However, by April 2022, Russian intensity has been decreased and with heavy sanctions the U.S. and West have applied on Russia, Russia has begun to gain such empathy with decrease on the Ukrainian side. [ABSTRACT FROM AUTHOR]

Sentiment Analysis is a highly crucial subfield in Natural Language Processing that attempts to extract the public sentiment from the accessible user opinions. This paper proposes a hybridized neural network based sentiment analysis framework using a modified term frequency-inverse document frequency approach. After preprocessing of data, the basic term frequency-inverse document frequency scheme is improved by introducing a non-linear global weighting factor. This improved scheme is combined with the k-best selection method to vectorize textual features. Next, the pre-trained embedding technique is employed for the mathematical representation of the textual features to process them efficiently by the Deep Learning methodologies. The embedded features are then passed to the deep neural network, consisting of Convolutional Neural Network and Long Short Term Memory. Convolutional Neural Networks can build hierarchical representations for capturing locally embedded features within the feature space, and Long Short Term Memory tries to recall useful historical information for sentiment polarization. This deep neural network finally provides the sentiment label. The proposed model is compared with different state-of-the-art baseline models in terms of various performance metrics using several datasets to demonstrate its efficacy. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a part of natural language processing, along with text mining. Over the years, sentiment analysis has become a key study area for researchers and industries all over the world. The major goal of this review is to overview the papers that have found the sentiment of the data under study over the past few years. Also, the various techniques and methods that have enabled us to solve the problem of sentiment analysis are looked into and put forward briefly. The articles are looked into considering the area of sentiment analysis in which the contributions are made, be it sentiment detection, dataset creation, or transfer learning. This would help the researchers to curate advanced and accurate models and methods for analyzing sentiment. Additionally, they would be given a quick rundown of current developments in this area of research. With this in mind, the literature study is conducted taking into account various applications, various machine learning algorithms, the type of data utilized for analysis purposes, and various performance measurements. Challenges and gaps based on all the contributions studied are summarized. [ABSTRACT FROM AUTHOR]

In today's world, recommendation systems play a vital role in customer analysis on social media, online businesses, e-commerce, etc. There are multiple sources of information on the Internet giving people a large set of suggestions and advice. This may create confusion for the accurate decision to the user and he/she may get lost in the competitive and growing market. A recommendation system is an essential part of e-commerce to supply the filtered relevant information asked by the customer. The major pitfalls of the existing recommendation system are flooding unnecessary recommendations and unpredictability about new products. Most of the recommendation systems rely on the purchase history of the customer and give suggestions for new products. Along with the history of the user's purchase, it is crucial to analyze various other activities such as browsing history, wish lists, reviews, ratings, and previously ordered items. An intelligent recommendation system using ensemble learning is presented in this paper to reduce duplicate and irrelevant recommendations for the customer. The experimental results indicate that there has been a significant improvement in the precision and recall of the recommendation system in comparison with the other conventional techniques. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a process of analysis, processing, induction, and reasoning of subjective text with emotional color. It is a research direction of Natural Language Processing (NLP). It is often used to extract the attitudes towards someone or something of people. That can help users find potential problems to improve or predict. As one of the main resources of online media data, film review information is often used as a dataset in the field of sentiment analysis. Researchers put forward many models in sentiment analysis to analyze the film review dataset. Accuracy, precision, recall rate, F1-scores are important standards to measure the quality of a model. To improve these criteria, a new model is proposed in this paper. The new model combines a bidirectional Long Short-Term Memory network (biLSTM) or a bidirectional Gated Recurrent Unit (biGRU) and an Enhanced Multi-Head Self-Attention mechanism. The Enhanced Multi-Head Self-Attention is a two-layer modified Transformer encoder. This modified Transformer encoder is that its masking operation and the last feedforward layer are removed. Besides, the loss function of this new model is the sum of the weighted root mean square error (RMSE) and the cross entropy loss. The operation of this sum can improve the ability of auto-encoder to reproduce. That can improve classification accuracy. The proposed model is an autoencoder classification model. In this model, biLSTM or biGRU are used as encoders and decoders at both ends of the network. Enhanced Multi-Head Self-Attention is used to encode the inter-sentence information as the middle hidden layer. A four-layer autoencoder network model is constructed to perform sentiment analysis on movie review in this paper. The movie review data sets (IMDB movie comment data set and SST-2 sentiment data set) are used in experiments. Experiment results show that the proposed model performs better in terms of accuracy, precision, recall rate, and F1-scores comparing with the baseline models. BiLSTM is better than biGRU by comparing the effect of them in the model. Finally, Bidirectional Encoder Representations from Transformers (BERT) is used in our method instead of word2vec as a pre-training structure. Compared with the baseline model based on BERT, the proposed model is better. [ABSTRACT FROM AUTHOR]

This paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers' user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback. [ABSTRACT FROM AUTHOR]

The Arabic language is a complex language with little resources; therefore, its limitations create a challenge to produce accurate text classification tasks such as sentiment analysis. The main goal of sentiment analysis is to determine the overall orientation of a given text in terms of whether it is positive, negative, or neutral. Recently, language models have shown great results in promoting the accuracy of text classification in English. The models are pre-trained on a large dataset and then fine-tuned on the downstream tasks. Particularly, XLNet has achieved state-of-the-art results for diverse natural language processing (NLP) tasks in English. In this paper, we hypothesize that such parallel success can be achieved in Arabic. The paper aims to support this hypothesis by producing the first XLNet-based language model in Arabic called AraXLNet, demonstrating its use in Arabic sentiment analysis in order to improve the prediction accuracy of such tasks. The results showed that the proposed model, AraXLNet, with Farasa segmenter achieved an accuracy results of 94.78%, 93.01%, and 85.77% in sentiment analysis task for Arabic using multiple benchmark datasets. This result outperformed AraBERT that obtained 84.65%, 92.13%, and 85.05% on the same datasets, respectively. The improved accuracy of the proposed model was evident using multiple benchmark datasets, thus offering promising advancement in the Arabic text classification tasks. [ABSTRACT FROM AUTHOR]

Nowadays, the internet and social media ease the process of reviewing products. Consumers expose their thoughts, opinions, and experiences about products and services on various forums, websites, and mobile apps. In effect, internet reviews become a decision-maker for many people before getting their desired goods. Actually, text sentiment analysis consists of extracting insights and sentiments from social texts and consumers' reviews. Hence, various organizations conduct this analysis in order to better understand the attitude as well as the feedback of their customers toward their products. Besides, many scientific researchers are also interested in the analysis of customers' reviews by labeling them into a set of sentiments using some text classification algorithms. The following paper provides a convolutional neural network (CNN) model to classify text reviews' sentiments as negative or positive. Also, we make a comparative analysis using our proposed CNN model and several models' representations of word embedding to get the most efficient model. The experiments are implemented on the Amazon reviews dataset, and the diverse model designs have achieved appropriate performances. Reached results discern the importance of including stop-word in sentiment analysis tasks, in fact, the stop words elimination can provoke an inaccurate prediction of sentiments. Practically, using stop words with the CNN model has improved the accuracy result by 2% opposing the CNN model that has ignored them. Furthermore, we procure that the employment of a random initialization approach provides better performance than supervised and embedding model vectors on large-scale datasets. Effectively, training the representation of word embedding allows the model to learn better features in less computation time. Moreover, our CNN model showed better performance than the baseline machine learning and deep learning methods and improved the accuracy of the CNN to 90% on the Amazon reviews dataset. [ABSTRACT FROM AUTHOR]

This paper focuses on text-based affective computing for Jopara, a code-switching language that combines Guarani and Spanish. First, we collected a dataset of tweets primarily written in Guarani and annotated them for three widely used dimensions in sentiment analysis: (a) emotion recognition, (b) humor detection, and (c) offensive language identification. Then, we developed several neural network models, including large language models specifically designed for Guarani, and compared their performance against off-the-shelf multilingual and Spanish pre-trained models for the aforementioned dimensions. Our experiments show that language models incorporating Guarani during pre-training or pre-fine-tuning consistently achieve the best results, despite limited resources (a single 24-GB GPU and only 800K tokens). Notably, even a Guarani BERT model with just two layers of Transformers shows a favorable balance between accuracy and computational power, likely due to the inherent low-resource nature of the task. We present a comprehensive overview of corpus creation and model development for low-resource languages like Guarani, particularly in the context of its code-switching with Spanish, resulting in Jopara. Our findings shed light on the challenges and strategies involved in analyzing affective language in such linguistic contexts. [ABSTRACT FROM AUTHOR]

In this paper, a novel method for analyzing the sentiments portrayed by Sanskrit text has been proposed. Sanskrit is one of the world's most ancient languages; however, natural language processing tasks such as machine translation and sentiment analysis have not been explored for it to the full potential because of the unavailability of sufficient labeled data. We solved this issue using a zero-shot learning-based cross-lingual sentiment analysis (CLSA) approach. The CLSA uses the resources from the source language to enhance the sentiment analysis of the target language having insufficient resources. The proposed work translates the text from Sanskrit, a language with insufficient labeled data, to English, with sufficient labeled data for sentiment analysis using a transformer model. A generative adversarial network-based strategy has been proposed to evaluate the maturity of the translations. Then a bidirectional long short-term memory-based model has been implemented to classify the sentiments using the embeddings obtained through translations. The proposed technique has achieved 87.50% accuracy for machine translation and 92.83% accuracy for sentiment classification. Sanskrit-English translations used in this work have been collected through web scraping techniques. In the absence of the ground-truth sentiment class labels, a strategy for evaluating the sentiment scores of the proposed sentiment analysis model has also been presented. A new dataset of Sanskrit text, along with their English translations and sentiment scores, has been constructed. [ABSTRACT FROM AUTHOR]

Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Social media content is mostly used for review, opinion, influence, or sentiment analysis. In this paper, we aim to extend sentiment and emotion analysis for detecting the stress of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to accomplish sentiment analysis with the aid of machine learning algorithms and a deep learning model, BERT for sentiment classification. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method for scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us to predict which topic is linked to the textual data. With the aid of these models, we will be able to detect the emotion of users online. Further, these emotions can be used to analyze stress or depression. In conclusion, the ML models and a BERT model have a very good detection rate. This research is useful for the well-being of one's mental health. The results are evaluated using various metrics at the macro and micro levels and indicate that the trained model detects the status of emotions based on social interactions. [ABSTRACT FROM AUTHOR]

Along with the significant growth of social media, individuals and companies are increasingly receiving public opinions which direct their decisions. Opinion mining, which is considered as a sub-field of natural language processing, information retrieval, and text mining, is the process of understanding the users' views from their comment, which have been represented as unstructured texts. Emergence of online social media has led to the production of a huge amount of user comments on websites, and thus, has raised opinion mining as a very useful and challenging problem. In this paper, an efficient preprocessing method for opinion mining is presented and will be used for analyzing users' comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Word2vec method which is a fast and accurate method have been also exploited to convert the words' arrays to numerical vectors. Machine learning methods, with supervised learning approach, have been applied on the obtained data after this fast and accurate preprocessing phase. Python and RapidMiner have been used to implement different opinion mining methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined use of the preprocessing method of this paper and support vector machine and artificial neural network have the highest accuracy compared to other methods. [ABSTRACT FROM AUTHOR]

Background: Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people's thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people's sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people's sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap. Methods: In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS. Results: This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people's sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted. Conclusion: Various applications of opinion mining techniques in mining people's sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people's sentiments based on text in cyberspace. Kansei approach can measure people's impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining. [ABSTRACT FROM AUTHOR]

The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models. Among these models, Transformer-based models such as BERT have become increasingly popular due to their state-of-the-art performance. However, these models are usually focused on English, leaving other languages to multilingual models with limited resources. This paper proposes a monolingual BERT for the Persian language (ParsBERT), which shows its state-of-the-art performance compared to other architectures and multilingual models. Also, since the amount of data available for NLP tasks in Persian is very restricted, a massive dataset for different NLP tasks as well as pre-training the model is composed. ParsBERT obtains higher scores in all datasets, including existing ones and gathered ones, and improves the state-of-the-art performance by outperforming both multilingual BERT and other prior works in Sentiment Analysis, Text Classification, and Named Entity Recognition tasks. [ABSTRACT FROM AUTHOR]

The purpose of this study is to describe the emotions that dominated the political vocabulary used on Twitter during the Covid-19 epidemic by the leaders of the major Italian political parties, including Forza Italia, Movimento 5 Stelle, Fratelli d'Italia, Italia Viva, Lega, and Partito Democratico. We developed a 4-step analysis model based on Natural Language Processing (NLP) that combines: (1) Exploratory Textual Data Analysis; (2) Emotion Recognition (ER) for Italian language; (3) cross-tabulation coding between leaders and emotions; (4) the multivariate approach of Correspondence Analysis in order to determine the associations between politicians and four categories of emotions: anger, fear, sadness, and joy. Specifically, we contrasted the language utilized by Covid-19 leaders throughout the first and second waves. The results reveal an intriguing shift in the emotions communicated through political discourse between the first and second waves. Giorgia Meloni's position as head of Fratelli d'Italia shifted significantly from fury to fear and grief, aligning her more with centrists and foreshadowing a serious rift in the right-wing and the balance of coalitions in the political arena. Giorgia Meloni became Italy's Prime Minister in September 2022. This political success shows that the sentiment analysis done in this paper could be a good way to predict the future. [ABSTRACT FROM AUTHOR]

These days, the world has become a non-denominational place due to cyberspace. Digital services such as search portals and social platforms have become intricate with the regular course of daily life. As a massive number of clients' thoughts are available on the internet, sentiment analysis has become one of the most prolific investigation arenas in natural language processing. Sarcasm detection is a vital phase in sentiment analysis because of the intrinsically vague nature of sarcasm. To subsidize as a resolution to the current ever-increasing arena of attention on the ever-increasing volume of digital data, this work proposes a novel voting-based ensemble approach for sarcasm detection utilizing deep learning techniques. This paper uses Bidirectional Encoder Representations from Transformers to build contextual word embeddings and feeds those to the network, which is an ensemble of four deep learning models. Convolutional Neural Network, Bidirectional Long Short-Term Memory, and parallel and sequential combinations of both are among the models. For classification, the acquired features from each model are experimented with four machine learning classifiers, such as Support Vector Machine, Least Squares Support Vector Machine, Multinomial Naive Bayes, and Random Forest, along with Sigmoid activation function. Out of these five, the optimal classifier is selected for each model. Finally, a majority voting is performed on the outputs of all four models to differentiate between sarcastic and non-sarcastic texts. The proposed approach has been assessed using two benchmark datasets containing English-language texts, namely, news headlines and the self-annotated reddit corpus. Experiments demonstrated that the proposed methodology produces an accuracy of 94.89% on the news headlines repository, which is a gain of 2.99%, and an F1-score of 80.49% on the self-annotated reddit corpus, which is an improvement of 0.52% over the previous state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is a study area focused on obtaining contextual polarity from the text. Currently, deep learning has obtained outstanding results in this task. However, much annotated data are necessary to train these algorithms, and obtaining this data is expensive and difficult. In the context of low-resource scenarios, this problem is even more significant because there are little available data. Transfer learning (TL) can be used to minimize this problem because it is possible to develop some architectures using fewer data. Language models are a way of applying TL in natural language processing (NLP), and they have achieved competitive results. Nevertheless, some models need many hours of training using many computational resources, and in some contexts, people and organizations do not have the resources to do this. In this paper, we explore the models BERT (Pretraining of Deep Bidirectional Transformers for Language Understanding), MultiFiT (Efficient Multilingual Language Model Fine-tuning), ALBERT (A Lite BERT for Self-supervised Learning of Language Representations), and RoBERTa (A Robustly Optimized BERT Pretraining Approach). In all of our experiments, these models obtain better results than CNN (convolutional neural network) and LSTM (Long Short Term Memory) models. To MultiFiT and RoBERTa models, we propose a pretrained language model (PTLM) using Twitter data. Using this approach, we obtained competitive results compared with the models trained in formal language datasets. The main goal is to show the impacts of TL and language models comparing results with other techniques and showing the computational costs of using these approaches. [ABSTRACT FROM AUTHOR]

The unprecedented growth in the use of social media platforms, where opinions and decisions are made and updated within seconds. Hence, Twitter is becoming a huge commercial interest for brands and companies to assess the sentiment of customers. Sentiment analysis tries to extract subjective opinions and sentiments from opinionated data using Natural Language Processing (NLP). Ontology-based analysis was primarily used to disambiguate the terms and get a high precision score for the emotive terms. In this paper, to improve the accuracy of sentiment analysis we modified the RoBERTa model to extract the more relevant contextualized information. Moreover, this modified RoBERTa is combined with RNN for effective sentiment classification. The proposed work attempts to find which words or phrases actually contribute to the particular sentiment as output by a modified RoBERTa model and this output of the RoBERTa model is fed as input for RNNs. The proposed model is experimented on the Twitter comment dataset. The proposed model experimented on various models such as single RNN, single layer LSTM, and Bi-directional LSTM and evaluated performance measures in terms of accuracy, precision, recall, and F1-score. Our proposed model performance significantly improved with respect to all other models in terms of accuracy, precision, recall, and F1-score. The experiments show that the proposed model not only increases the Jaccard similarity score but also improves different RNN performance when compared to existing state-of-the-art models. The proposed approach obtained a maximum accuracy of 84.6% which is a huge improvement and also evaluated comparison analysis of Simple RNN, Single-LSTM, and Bi-LSTM on full text and selected test. Our proposed modified ROBERTa performance is superior with selected text and full text. Finally, the statistical paired T-test is performed between the proposed model, and other models such as simple RNN, One layer RNN is giving evidence that the proposed model performance is superior with 95% confidence and p < 0.05 . [ABSTRACT FROM AUTHOR]

To eliminate the concerns of cold-start and scalability within the filtering, collaborative recommendation system for a hotel under the ranking list for the customer; this study proposed an extensive data analysis for such an application. Alongside, the application is very user-friendly. Technology keeps growing daily and is also very helpful in the future. This paper brings out the innovation. This approach configures the latest variant of CNN termed as Capsule Network (CapsNet) to recommend the best suited hotel based on user collaboration. With the help of this application, we can make precise and accurate recommendations to the user. Besides, generally employed, the procedure adopted for the model recommended integrated with the filtering practice that includes analyzing user's preferences and making product or service recommendations through a similarity index among the ratings specified in the database. Finally, the merging technique employed in the system is discussed and evaluated on standard data sets based on the general architectural design of the recommendation system. An enhanced strategy attains fusion idea of filtering technique in collaborative to classified data for addressing this issue. To put the proposed approach to the test, we employ hotel recommendation data. Also, the outcome proved the best findings of the comprehensive list taken as the top listed hotels from which the scale has been considered through the proposed method. [ABSTRACT FROM AUTHOR]

In the digital era, social media websites have emerged as powerful tools for sharing emotions worldwide, and significantly impacting people's social and personal lives. People share their feelings and perspectives in the form of texts, images, emojis, audio, emoticons, and videos. Among the popular social media platforms, Twitter stands out, generating an enormous quantity of unstructured textual data. Due to its strict character limit of 280, Tweets are particularly prone to information diversity, text sarcasm, word sense ambiguity, indirect negation, and other problems. Consequently, identifying and categorizing actual emotions from these textual data becomes a challenging task. To address these challenges; this research paper improved the performance accuracy of the existing HeBiLSTM model by adding one additional Bi-LSTM hidden layer, named as Improved GloVe-HeBiLSTM model. Additionally, the Authors proposed a Hierarchical Bi-CuDNNLSTM with NVIDIA CUDA deep neural network library (named HeBi-CuDNNLSTM) as a practical framework for analyzing actual emotions of textual data. The Improved GloVe-HeBiLSTM and proposed HeBi-CuDNNLSTM models have been trained and validated with emotions-dataset-for-NLP dataset and evaluated with various performance measures such as accuracy, recall, precision, and F 1 -score. Performance accuracy of the Improved GloVe-HeBiLSTM and the proposed HeBi-CuDNNLSTM frameworks have been compared with existing HeBiLSTM (89%) and recent baseline models, mainly GloVe-CNN-LSTM (92.75%), GloVe-BiLSTM (93.10%), GloVe-CNN-BiLSTM (92.65%), and GloVe-CNN-BiGRU (93.25%). Therefore the Improved GloVe-HeBiLSTM and proposed HeBi-CuDNNLSTM models outperform the existing HeBiLSTM and other baseline models with an accuracy rate of 93.70% and 94.20%, respectively. [ABSTRACT FROM AUTHOR]

Span-level method achieves competitive results in Aspect Sentiment Triplet Extraction (ASTE) by enumerating all possible spans. However, previous span-level methods fail to exploit syntactic information to identify the correspondence between aspect terms and opinion terms, which makes the extracted triplets inaccurate. In this paper, we propose a syntactic and semantic dual-enhanced bidirectional network (SSBN) for ASTE task. By constructing word dependencies as a graph and embedding them into features to capture syntactic information more effectively in bidirectional network. Furthermore, we design a pruning strategy that uses part-of-speech information to alleviate the problem of identifying potential aspects and opinions from a large number of spans. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate the effectiveness of the SSBN model. [ABSTRACT FROM AUTHOR]

Locating bug code snippets (short for BugCode) has been a complex problem throughout the history of software security, mainly because the constraints that define BugCode are obscure and hard to summarize. Previously, security analysts attempted to define such constraints manually (e.g., limiting buffer size to detect overflow), but were limited to the types of BugCode. Recent researchers address this problem by extracting constraints from program documentation, which shows the potential for API misuse. But for bugs beyond the scope of API misuse, such an approach becomes less effective since the corresponding constraints are not defined in documents, not to mention the programs without documentation In this paper, inspired by the fact that expert programmers often correct the BugCode on open forums such as StackOverflow, we design an approach to automatically extract knowledge from StackOverflow and leverage it to detect BugCode. As we all know, the contexts in StackOverflow come from ordinary developers. Their writing tends to be loosely organized and in various styles, which are more challenging to analyze than program documentation. To address the challenges, we design a custom tokenization approach to segment sentences and employ sentiment analysis to find the Controversial Sentences (CSs) that typically contain the constraints we need for code analysis. Then we use constituency parsing to extract knowledge from CSs, which helps locate BugCode. We evaluated our system on 41,144 comments from the questions tagged with Java and Android. The results show that our approach achieves 95.5% precision in discovering CSs. We have discovered 276 pieces of BugCode proved to be true through manual validation including an assigned CVE. 89.3% of the discovered bugs remained in the current version of answers, which are unknown to users. [ABSTRACT FROM AUTHOR]

Despite the positive impact of games for health on players' health, users tend to stop playing them after a short period of time, leading benefits to fade. It is therefore important to understand how to sustain interest and, in this way, preserve the health benefits of games for health. This could be achieved by continuously reviewing user feedback after product launch and using this information to inform (re)design and better address user needs. With the growth of social media, user opinions became widely available in public forums. This abundance of information affords us the possibility of, through the application of natural language processing and sentiment analysis techniques, tapping into user opinions and automatically analysing and extracting knowledge from them. This paper introduces a methodology that analyses user comments posted on YouTube about the Just Dance game, to automatically extract information about Usability, User Experience (UX), and Perceived Health Impacts related to Quality of Life (H-QoL). In doing so, the methodology uses a pre-established vocabulary, based on the English lexicon and its semantic relations, to annotate the presence of 38 concepts (five of Usability, 18 of UX, and 15 of H-QoL) and to analyse sentiment. The results of the information extraction and processing are displayed on a dashboard that allows for the exploration and browsing of the results, which can be useful to better understand the opinions and impacts perceived by users and to inform the (re)design of games for health. The methodology proposed builds upon over 500,000 user comments collected from over 32,000 videos. [ABSTRACT FROM AUTHOR]