Search

Moving beyond classical single-label classification tasks proves to be crucial nowadays since the majority of real-life problems are inherently multi-label. Various tasks require involving, for one single instance as an input, several classes simultaneously or none of them. Particularly, detecting abusive texts in Arabic social networks intuitively carries several manifestations with regard to the targets' referred characteristics like gender, race, sexual orientation, etc. Existing multi-label algorithms either invest considerable complexity to handle labels' correlation or explicitly ignore this information to deal with each label as an independent binary problem. To remedy the abovementioned problems, this paper proposes a new multi-label classification solution based on deep neural networks. The main idea is to exploit the correlation between all the dataset's labels to break it into ordered subsets. Then, follow that order to implement a chain of BERT classifiers. Each classifier is therefore trained on a corresponding subset and passes its output via feature space to the following one. For a new instance, we gather all the classifiers' results and pick the best one for each class. Experiments showed that our approach significantly outperformed the single classifier's method using BERT as well as other neural network architectures on our Arabic abusive language dataset. 91%,76%, and 82% were achieved respectively for precision, recall, and F1-score. Moreover, we further investigated the performance of our approach on Kaggle multi-label research paper dataset and achieved very promising results. [ABSTRACT FROM AUTHOR]

With the fast growth of online content and social media platforms, the amount of user- generated data available in the form of product evaluations, social media postings, and online debates has rapidly expanded. It has become essential for organizations to comprehend client attitudes, make wise decisions, and improve their goods and services by analysing the sentiment indicated in this data. This paper aims to develop a sentiment analysis system for product reviews using URLs of particular item as data sources. It utilizes web scraping techniques to collect textual information from web pages. Natural Language Processing (NLP) techniques are applied for data pre-processing, including tokenization and stop word removal. Machine learning models like Support Vector Classifier (SVC), Random Forest, KNN, Logistic Regression, Naive Bayes and Deep learning models like LSTM and GRU are used for sentiment analysis. Performance measures such as recall, accuracy, and precision are employed to evaluate the models. High scores were consistently achieved by the LSTM and GRU models across all measures, demonstrating their potency in successfully capturing sequential relationships. With strong F-1 ratings of 91% and 90%, respectively, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) regularly beat other models, demonstrating their outstanding precision-recall balance. Conversely, K-Nearest Neighbor (KNN) presents a significant trade-off, offering a blazing-fast training time of 0.01 seconds but demanding an extensive 102.34 seconds for prediction, resulting in a lower F-1 score of 47%. Support Vector Machine (SVM) and Logistic Regression achieve competitive F-1 scores of 85% and 84%, respectively, with relatively slow training and prediction times, striking a balance between performance and efficiency. Naive Bayes and Random Forest, while respectable in accuracy, fall slightly behind with F-1 scores of 71% and 80%, respectively, highlighting the significance of taking into account both performance and computing efficiency when choosing the best approach. Through this research, valuable insights are gained into the effectiveness and accuracy of different algorithms in sentiment analysis. The findings contribute to the understanding of sentiment analysis techniques and offer guidance for selecting the most suitable algorithm for URL-based sentiment analysis applications. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a cognitive tool to extract the emotional tone of a piece of text. It is an area of research that is actively pursued in Natural Language Processing. Social media, forums and blogs, among other places, have seen a massive surge in the number of personalized reviews since the emergence of Internet-based applications. Sentiment analysis is primarily concerned with the classification and prediction of users' thoughts and emotions from these reviews. In recent years, numerous deep learning techniques have emerged to achieve this task. This paper provides a technical summary of Sentiment Analysis using a Bidirectional LSTM network. This model is capable of handling long-term dependencies by introducing memory into the model for making better predictions. The Amazon Product Review dataset served as the source of data for this research. Concretely, the analysis is performed on 104,975 product reviews reflecting users' attitudes toward mobile electronics products. The proposed model attempts to classify the reviews into two categories: positive and negative. Finally, the paper outlines the results of the analysis and suggests potential avenues for future research. [ABSTRACT FROM AUTHOR]

Suicidal ideation detection from social media is an evolving research with great challenges. Many of the people who have the tendency to suicide share their thoughts and opinions through social media platforms. As part of many researches it is observed that the publicly available posts from social media contain valuable criteria to effectively detect individuals with suicidal thoughts. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide. This can be achieved by identifying the sudden changes in a user's behavior automatically. Natural language processing techniques can be used to collect behavioral and textual features from social media interactions and these features can be passed to a specially designed framework to detect anomalies in human interactions that are indicators of suicidal intentions. We can achieve fast detection of suicidal ideation using deep learning and/or machine learning based classification approaches. For such a purpose, we can employ the combination of LSTM and CNN models to detect such emotions from posts of the users. In order to improve the accuracy, some approaches like using more data for training, using attention model to improve the efficiency of existing models etc. could be done. This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect any underlying suicidal intentions. During evaluations, the proposed model demonstrated an accuracy of 90.3% and an F1-score of 92.6%, which is greater than the baseline models. [ABSTRACT FROM AUTHOR]

Sarcasm is a complex linguistic phenomenon involving humor, criticism, or phrases that convey the opposite meaning, mask true feelings, and play pivotal roles in various aspects of communication. Therefore, identifying sarcasm is essential for sentiment analysis, social media monitoring, and customer service, as it enables a better understanding of public sentiment. Moreover, social media has become a primary platform for people to express their feelings and opinions and provide feedback to businesses and service providers. Misinterpreting sarcasm in customer feedback can lead to incorrect responses and actions. However, accurately detecting sarcasm is challenging because it depends on context, cultural factors, and inherent ambiguity. Despite the plenty of research and resources in Machine Learning (ML) for detecting sarcasm in English, including Deep Learning (DL) techniques, there is still a shortage of research in sarcasm detection in Arabic, particularly in DL methodologies and available sarcastic datasets. This paper constructed a new Arabic sarcastic corpus and fine-tuned three pre-trained Arabic transformer-based Language Models (LM) for Arabic sarcasm detection. We also proposed a hybrid DL approach for sarcasm detection that combines static and contextualized representations using pre-trained LM, such as Word2Vec word embeddings and Bidirectional Encoder Representations from Transformers (BERT) models pretrained on Arabic resources. The proposed enhanced hybrid deep learning approach outperforms state-of-the-art models by 8% on a shared benchmark dataset and achieves a 5% improvement in F1-score on another. [ABSTRACT FROM AUTHOR]

Abstract Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, online social media platforms such as Twitter facilitate the exchange of information among people. However, the prevalence of "infodemic" such as online hate speech has exacerbated social rifts, discrimination, prejudice and even hate crimes. Timely and effective detection of the hate speech will help create a healthy public opinion environment. Most of the current COVID-19-related hate speech research focuses on a single language, such as English. In this paper, we introduce a cross-lingual transfer learning method, aiming to contribute to hate speech detection in low-resource languages. We propose a deep learning based model to classify hate speech with a pre-trained language model for multilingual text embedding. Data augmentation and cross-lingual contrastive learning are then utilized to further improve the performance of cross-lingual knowledge transfer. To evaluate our method, we collected three publicly available annotated COVID-19-related hate speech datasets on Twitter, i.e., two in English and one in German. Furthermore, a Chinese dataset based on Weibo is constructed to expand multilingual data. The experimental results across three languages illustrate the effectiveness of our method for cross-lingual hate speech detection. Test F1-scores of our method for English, Chinese, German as transfer target languages can reach up to 0.728, 0.799 and 0.612 respectively, which are on average better than other baselines. • Suggested a cross-lingual COVID-19-related hate speech detection problem. • Introduced cross-lingual contrastive learning. • Validated model performance on datasets in three languages. [ABSTRACT FROM AUTHOR]

Regret is a universal emotion that arises from sadness, disappointment, or remorse about something that has occurred or that one has done or failed to do in the past. It typically involves wishing that a different decision had been made or that a different action had been taken. Although regret has been studied in various contexts such as psychology, neuroscience, and philosophy, its expression and analysis on social media using natural language processing techniques is a relatively recent research topic that warrants further investigation. In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication. • Regret is an emotion that has a significant impact on behavior and decision-making. • Regret is often triggered by actions or inactions in the past. • Reddit's users often regret their actions about relationships in the past. • Effectiveness of GloVe in representing the meaning and context of words. [ABSTRACT FROM AUTHOR]

Abstract Recently, the vast use of social media and the high availability of internet access have produced a considerably different textual data from the formal and standard data on the Web. This includes various Arabic dialectal languages, which are the native spoken languages of Arabic speakers. The presence of textual Arabic dialectal languages on the Web has brought many new opportunities as well as challenges for machine learning and Arabic language processing. The identification of this type of informal data has its crucial effect on several applications such as sentiment analysis and machine translation. However, the standard NLP tools developed for traditional data fall short due to nature of dialectal textual data. Deep learning tools have proven to be very effective in processing social Media dialectal text. In this paper, we consider a variety of deep learning models for the automatic classification of Arabic dialectal text. We use a free large manually-annotated dataset known as Arabic Online Commentary (AOC), which includes several Dialectal Arabic (DA) along with the Modern Standard Arabic (MSA), [3]. We consider the most frequent dialects in the dataset. Namely, the Egyptian (EGP), Levantine (LEV), and Gulf –including Iraqi - (GLF). Four different deep neural network models have been implemented to examine the Arabic dialectal classification problem for each pair of the 3 dialects (binary classification experiments) as well as one ternary-classification experiment including all dialects together. The results show a varying but promising performance of the models for each pair of dialects. Furthermore, a closer examination on the manually-annotated AOC dataset has been carried out and hence, we conclude that there is a serious demand for a thorough refinement and review of the AOC annotated sentences as it is an important benchmark dataset in the field. [ABSTRACT FROM AUTHOR]

• Three datasets are merged to build a larger, more diversified spam dataset. • Data balancing is done using SmoteTomek and NearMiss data resampling techniques. • Proposed Hard and Soft voting ensemble models for performance improvement. • Several DL approaches are experimented with GloVe and FastText word embeddings. • Proposed FCB-SA hybrid DL approach outperformed other DL approaches. Cybercriminals use social media platforms to disseminate spam, misleading facts, fake news, and malicious links. Blocking such deceptive social media spam is essential. However, extracting relevant features from social networks is challenging due to privacy and time constraints. Traditional frequency-based word representation techniques are time-consuming and inefficient in producing contextual word vectors. Word embeddings and deep learning models have recently shown good results in text classification. Also, most existing approaches assumed balanced class distribution, which is false for most real-world datasets. In this paper, an attempt is made to advance the performance of the social spam detection system by leveraging dataset balancing, advanced word embedding techniques, machine learning, and deep learning approaches with the self-attention mechanism. In the proposed framework, the datasets are balanced using NearMiss and SmoteTomek techniques to feed several machine-learning models. Later, the baseline ML models and proposed voting-based ensemble models are evaluated on imbalanced and balanced datasets. For the proposed deep learning-based hybrid approaches, embeddings are generated using GloVe and FastText word embeddings on the balanced combined dataset and passed into the deep neural network comprised of Conv1D and Bi-directional recurrent neural network layers with the self-attention mechanism for improved context understanding and effective results. This study examines hybrid approaches for detecting social spam using imbalanced social network data and picks the optimum combination. Besides, Machine learning ensembles, word embeddings, deep learning with hyper-parameter optimization, and a self-attention method are compared thoroughly. Experiments and comparisons with other techniques show that the proposed hybrid framework with deep learning-based approaches achieves better performance. [ABSTRACT FROM AUTHOR]

Over the years, there has been a rise in the number of fabricated and fake news stories that utilize both textual and visual information formats. This coincides with the increased likelihood that users will acquire their news from websites and social media platforms. While there has been various research into the detection of fake news in text using machine learning techniques, less attention has been paid to the problem of multimedia data fabrication. In this paper, we propose a Stylometric, and Semantic similarity oriented for Multimodal Fake News Detection (SSM). There are five distinct modules that make up our methodology: Firstly, we used a Hyperbolic Hierarchical Attention Network (Hype-HAN) for extracting stylometric textual features. Secondly, we generated the news content summary and computed the similarity between Headline and summary. Thirdly, semantic similarity is computed between visual and textual features. Fourthly, images are analyzed for forgery. Lastly, the extracted features are fused for final classification. We have tested SSM framework on three standard fake news datasets. The results indicated that our suggested model has outperformed the base line and state-of-the-art methods and is more likely to detect fake news in complex environments. [ABSTRACT FROM AUTHOR]

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making. • Bi-LSTM sentiment detection model is developed to classify COVID-19 social comments. • Various scenarios to help counter negative COVID-19 comments and reduce their impact. • Comparative analysis of our approach with most popular models is presented. • Word embedding followed by Bi-LSTM provides better performance on Sentiment Analysis. [ABSTRACT FROM AUTHOR]

As the prevalence of social media on the Internet, opinion mining has become an essential approach to analyzing so many data. Various applications appear in a wide range of industrial domains. Meanwhile, opinions have diverse expressions which bring along research challenges. Both of the practical demands and research challenges make opinion mining an active research area in recent years. In this paper, we present a review of Natural Language Processing (NLP) techniques for opinion mining. First, we introduce general NLP techniques which are required for text preprocessing. Second, we investigate the approaches of opinion mining for different levels and situations. Then we introduce comparative opinion mining and deep learning approaches for opinion mining. Opinion summarization and advanced topics are introduced later. Finally, we discuss some challenges and open problems related to opinion mining. [ABSTRACT FROM AUTHOR]

Twenty-four studies on twenty-three distinct languages and eleven social media illustrate the steady interest in deep learning approaches for multilingual sentiment analysis of social media. We improve over previous reviews with wider coverage from 2017 to 2020 as well as a study focused on the underlying ideas and commonalities behind the different solutions to achieve multilingual sentiment analysis. Interesting findings of our research are (i) the shift of research interest to cross-lingual and code-switching approaches, (ii) the apparent stagnation of the less complex architectures derived from a backbone featuring an embedding layer, a feature extractor based on a single CNN or LSTM and a classifier, (iii) the lack of approaches tackling multilingual aspect-based sentiment analysis through deep learning, and, surprisingly, (iv) the lack of more complex architectures such as the transformers-based, despite results suggest the more difficult tasks requires more elaborated architectures. • Review of applications of Deep Learning to tackle Multilingual Sentiment Analysis. • Fast-growing interest in this field, 24 related papers since 2017 to 2020. • Coverage of 23 different languages and 11 social media data or corpus. • Mixed performance, but word embeddings and CNN or LSTM as trending choices. • Embeddings>feature extractor>classifier, prevailing architecture except for aspect SA. [ABSTRACT FROM AUTHOR]

Undoubtedly, social media, such as Facebook and Twitter, constitute a major part of our everyday life due to the incredible possibilities they offer to their users. However, Twitter and generally online social networks (OSNs) are increasingly used by automated accounts, widely known as bots, due to their immense popularity across a wide range of user categories. Their main purpose is the dissemination of fake news, the promotion of specific ideas and products, the manipulation of the stock market and even the diffusion of sexually explicit material. Therefore, the early detection of bots in social media is quite essential. In this paper, two methods are introduced targeting this that are mainly based on Natural Language Processing (NLP) to distinguish legitimate users from bots. In the first method, a feature extraction approach is proposed for identifying accounts posting automated messages. After applying feature selection techniques and dealing with imbalanced datasets, the subset of features selected is fed in machine learning algorithms. In the second method, a deep learning architecture is proposed to identify whether tweets have been posted by real users or generated by bots. To the best of the authors' knowledge, there is no prior work on using an attention mechanism for identifying bots. The introduced approaches have been evaluated over a series of experiments using two large real Twitter datasets and demonstrate valuable advantages over other existing techniques targeting the identification of malicious users in social media. [ABSTRACT FROM AUTHOR]

Adverse drug reaction events have become one of the main causes of patient death. Since traditional post-marketing surveillance systems based on spontaneous reports have a serious underreporting issue, in recent years research on the detection of adverse reaction events using social media such as Twitter as a data source has attracted increasing attention in recent year. Deep learning models usually rely on a large number of training samples. However, due to the characteristics of user-generated content and the time-consuming data annotation process, related research is faced with the problems caused by small-scale annotated datasets, which restricts deep learning models in achieving satisfactory results. Accordingly, we introduce two regularization methods are introduced at the representation level, i.e., graph embedding-based data augmentation and adversarial training, to improve the performance of detecting adverse events under such conditions. Besides, the applicable scope of these two methods is analyzed and discussed through experiments. Combined with the convolutional neural network, this paper proposes an adverse drug event detection framework that can make full use of the methods. [ABSTRACT FROM AUTHOR]

• New method to semi-automatically build labeled stance detection datasets from Twitter. • Translation strategies outperform zero-shot approaches when data is translated to a high-resourced language. • User-based information helps to label individual tweets. • Our method is applicable to quickly and cheaply generate labeled Twitter-based data. Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partially due to the fact that manually annotating a corpus of social media texts is a difficult, slow and costly process. Furthermore, as stance is a highly domain- and topic-specific phenomenon, the need for annotated data is specially demanding. As a result, most of the manually labeled resources are hindered by their relatively small size and skewed class distribution. This paper presents a method to obtain multilingual datasets for stance detection in Twitter. Instead of manually annotating on a per tweet basis, we leverage user-based information to semi-automatically label large amounts of tweets. Empirical monolingual and cross-lingual experimentation and qualitative analysis show that our method helps to overcome the aforementioned difficulties to build large, balanced and multilingual labeled corpora. We believe that our method can be easily adapted to easily generate labeled social media data for other Natural Language Processing tasks and domains. [ABSTRACT FROM AUTHOR]

• Developed browser plugins to visualize hate speech live on Facebook & Twitter. • Designed proof of concepts for personalized dashboards for Twitter & Instagram. • Optimized classifiers for Hate Speech with tests on standard datasets. • Application areas for the tool are discussed. The multi-fold growth of the social media user-base fuelled a substantial increase in the amount of hate speech posts on social media platforms. The enormous data volume makes it hard to capture such cases and either moderate or delete them. This paper presents an approach to detect and visualize online aggression, a special case of hate speech, over social media. Aggression is categorized into overtly aggressive (OAG), covertly aggressive (CAG), and non-aggressive labels (NAG). We have designed a user interface based on a web browser plugin over Facebook and Twitter to visualize the aggressive comments posted on the Social media user's timelines. This plugin interface might help to the security agency to keep a tab on the social media stream. It also provides citizens with a tool that is typically only available for large enterprises. The availability of such a tool alleviates the technological imbalance between industry and citizens. Besides, the system might be helpful to the research community to create further tools and prepare weakly labeled training data in a few minutes using comments posted by users on celebrity's Facebook, Twitter timeline. We have reported the results on a newly created dataset of user comments posted on Facebook and Twitter using our proposed plugins and the standard Trolling Aggression Cyberbullying 2018 (TRAC) dataset in English and code-mixed Hindi. Various classifiers like Support Vector Machine (SVM), Logistic regression, deep learning model based on Convolution Neural Network (CNN), Attention-based model, and the recently proposed BERT pre-trained language model by Google AI, have been used for aggression classification. The weighted F1-score of around 0.64 and 0.62 is achieved on TRAC Facebook English and Hindi datasets while on Twitter English and Hindi datasets, the weighted F1-score is 0.58 and 0.50, respectively. [ABSTRACT FROM AUTHOR]