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Sentiment analysis is a critical subfield of natural language processing that focuses on categorizing text into three primary sentiments: positive, negative, and neutral. With the proliferation of online platforms where individuals can openly express their opinions and perspectives, it has become increasingly crucial for organizations to comprehend the underlying sentiments behind these opinions to make informed decisions. By comprehending the sentiments behind customers' opinions and attitudes towards products and services, companies can improve customer satisfaction, increase brand reputation, and ultimately increase revenue. Additionally, sentiment analysis can be applied to political analysis to understand public opinion toward political parties, candidates, and policies. Sentiment analysis can also be used in the financial industry to analyze news articles and social media posts to predict stock prices and identify potential investment opportunities. This paper offers an overview of the latest advancements in sentiment analysis, including preprocessing techniques, feature extraction methods, classification techniques, widely used datasets, and experimental results. Furthermore, this paper delves into the challenges posed by sentiment analysis datasets and discusses some limitations and future research prospects of sentiment analysis. Given the importance of sentiment analysis, this paper provides valuable insights into the current state of the field and serves as a valuable resource for both researchers and practitioners. The information presented in this paper can inform stakeholders about the latest advancements in sentiment analysis and guide future research in the field. [ABSTRACT FROM AUTHOR]

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. [ABSTRACT FROM AUTHOR]

The advent of online platforms and services has revolutionized communication, enabling users to share opinions and ideas seamlessly. However, this convenience has also brought about a surge in offensive and harmful language across various communication mediums. In response, social platforms have turned to automated methods to identify offensive content. A critical research question emerges when investigating the role of specific text spans within comments in conveying offensive characteristics. This paper conducted a comprehensive investigation into detecting offensive text spans in Romanian language comments using Transformer encoders and Large Language Models (LLMs). We introduced an extensive dataset of 4800 Romanian comments annotated with offensive text spans. Moreover, we explored the impact of varying model sizes, architectures, and training data volumes on the performance of offensive text span detection, providing valuable insights for determining the optimal configuration. The results argue for the effectiveness of BERT pre-trained models for this span-detection task, showcasing their superior performance. We further investigated the impact of different sample-retrieval strategies for few-shot learning using LLMs based on vector text representations. The analysis highlights important insights and trade-offs in leveraging LLMs for offensive-language-detection tasks. [ABSTRACT FROM AUTHOR]

Sentiment analysis on social media platforms (i.e., Twitter or Facebook) has become an important tool to learn about users' opinions and preferences. However, the accuracy of sentiment analysis is disrupted by the challenges of natural language processing (NLP). Recently, deep learning models have proved superior performance over statistical- and lexical-based approaches in NLP-related tasks. Word embedding is an important layer of deep learning models to generate input features. Many word embedding models have been presented for text representation of both classic and context-based word embeddings. In this paper, we present a comparative analysis to evaluate both classic and contextualized word embeddings for sentiment analysis. The four most frequently used word embedding techniques were used in their trained and pre-trained versions. The selected embedding represents classical and contextualized techniques. Classical word embedding includes algorithms such as GloVe, Word2vec, and FastText. By contrast, ARBERT is used as a contextualized embedding model. Since word embedding is more typically employed as the input layer in deep networks, we used deep learning architectures BiLSTM and CNN for sentiment classification. To achieve these goals, the experiments were applied to a series of benchmark datasets: HARD, Khooli, AJGT, ArSAS, and ASTD. Finally, a comparative analysis was conducted on the results obtained for the experimented models. Our outcomes indicate that, generally, generated embedding by one technique achieves higher performance than its pretrained version for the same technique by around 0.28 to 1.8% accuracy, 0.33 to 2.17% precision, and 0.44 to 2% recall. Moreover, the contextualized transformer-based embedding model BERT achieved the highest performance in its pretrained and trained versions. Additionally, the results indicate that BiLSTM outperforms CNN by approximately 2% in 3 datasets, HARD, Khooli, and ArSAS, while CNN achieved around 2% higher performance in the smaller datasets, AJGT and ASTD. [ABSTRACT FROM AUTHOR]

With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the use of fake news. The emergence of this harmful phenomenon has managed to polarize society and manipulate public opinion on particular topics, e.g., elections, vaccinations, etc. Such information propagated on social media can distort public perceptions and generate social unrest while lacking the rigor of traditional journalism. Natural Language Processing and Machine Learning techniques are essential for developing efficient tools that can detect fake news. Models that use the context of textual data are essential for resolving the fake news detection problem, as they manage to encode linguistic features within the vector representation of words. In this paper, we propose a new approach that uses document embeddings to build multiple models that accurately label news articles as reliable or fake. We also present a benchmark on different architectures that detect fake news using binary or multi-labeled classification. We evaluated the models on five large news corpora using accuracy, precision, and recall. We obtained better results than more complex state-of-the-art Deep Neural Network models. We observe that the most important factor for obtaining high accuracy is the document encoding, not the classification model's complexity. [ABSTRACT FROM AUTHOR]

With the increased popularity of social media platforms such as Twitter or Facebook, sentiment analysis (SA) over the microblogging content becomes of crucial importance. The literature reports good results for well-resourced languages such as English, Spanish or German, but open research space still exists for underrepresented languages such as Romanian, where there is a lack of public training datasets or pretrained word embeddings. The majority of research on Romanian SA tackles the issue in a binary classification manner (positive vs. negative), using a single public dataset which consists of product reviews. In this paper, we respond to the need for a media surveillance project to possess a custom multinomial SA classifier for usage in a restrictive and specific production setup. We describe in detail how such a classifier was built, with the help of an English dataset (containing around 15 , 000 tweets) translated to Romanian with a public translation service. We test the most popular classification methods that could be applied to SA, including standard machine learning, deep learning and BERT. As we could not find any results for multinomial sentiment classification (positive, negative and neutral) in Romanian, we set two benchmark accuracies of ≈78% using standard machine learning and ≈81% using BERT. Furthermore, we demonstrate that the automatic translation service does not downgrade the learning performance by comparing the accuracies achieved by the models trained on the original dataset with the models trained on the translated data. [ABSTRACT FROM AUTHOR]

Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets. [ABSTRACT FROM AUTHOR]

Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people's perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%. [ABSTRACT FROM AUTHOR]

Most people nowadays depend on the Web as a primary source of information. Statistical studies show that young people obtain information mainly from Facebook, Twitter, and other social media platforms. By relying on these data, people may risk drawing the incorrect conclusions when reading the news or planning to buy a product. Therefore, systems that can detect and classify sentiments and assist users in finding the correct information on the Web is highly needed in order to prevent Web surfers from being easily deceived. This paper proposes an intensive study regarding domain-independent classification models for sentiment analysis that should be trained only once. The study consists of two phases: the first phase is based on a deep learning model which is training a neural network model once after extracting robust features and saving the model and its parameters. The second phase is based on applying the trained model on a totally new dataset, aiming at correctly classifying reviews as positive or negative. The proposed model is trained on the IMDb dataset and then tested on three different datasets: IMDb dataset, Movie Reviews dataset, and our own dataset collected from Amazon reviews that rate users' opinions regarding Apple products. The work shows high performance using different evaluation metrics compared to the stat-of-the-art results. [ABSTRACT FROM AUTHOR]

The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post's content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human beings. To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post's subjectivity and the stance provided by the replies it has received to date, letting the user decide whether (s)he trusts or does not trust the provided information. To this end, we fine-tuned state-of-the-art transformer-based language models and compared their performance with previous related work on stance detection and subjectivity analysis. Finally, we discuss the obtained results. [ABSTRACT FROM AUTHOR]

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f 1 score of our model with different configurations and with a baseline classifier. We obtained an f 1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%. [ABSTRACT FROM AUTHOR]

In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors. [ABSTRACT FROM AUTHOR]

In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text. [ABSTRACT FROM AUTHOR]

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset. [ABSTRACT FROM AUTHOR]