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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]

Classifying textual emotion plays a critical role in several HCI applications where the text is utilized as a central means of communication such as messages, reviews, blogs and other Web 2.0 platforms. The extensive usage of the Internet has emerged as an unprecedented means for people to express their feelings or emotion on blogs, social media, and e-commerce sites in recent years. Most of the emotions displayed on the online platforms are in textual forms (such as posts, tweets, comments and reviews), which are unorganized and time-consuming to structured due to their disordered forms. Although several emotion analysis tools are available in high-resource languages, it is critical to developing an automatic emotion classification system for low-resource languages, including Bengali, due to its constrained resources. This paper presents an ML-based ensemble method to classify six primary textual emotions (anger, fear, disgust, sadness, surprise and joy) from Bengali texts. An emotion corpus containing 8047 Bengali texts is developed to perform the textual emotion classification task. This work investigates eight standard ML-based techniques such as logistic regression (LR), multinomial naive Bayes (MNB), support vector machine (SVM), random forest (RF), decision tree (DT), K-nearest neighbour (KNN) and adaptive boosting (AdaBoost) and an ensemble method (a combination of LR, RF, SVM) with Bag of words (BoW) and tf-idf feature extraction techniques. The experimental result demonstrates that the ensemble with tf-idf achieved the highest weighted f 1 -score of 62.39% compared to other methods. [ABSTRACT FROM AUTHOR]

The usage of digital/social media is increasing day by day with the advancement of technology. People in the twenty-first century are being raised in an internet-enabled world with social media. Communication has been just one button click. Even though there are plenty of opportunities with digital media people tend to misuse it. People spread hatred toward a person in social networking. Cyberbullying affects people in different aspects. It doesn't affect only for health, there are more different aspects which will lead life to a threat. Cyberbullying is a worldwide modern phenomenon which humans cannot avoid hundred percent but can be prevented. Most existing solutions have shown techniques/approaches to detect cyberbullying, but they are not freely available for end-users to use. They haven't considered the evolution of language which makes a big impact on cyberbullying text. This paper describes a system for automatic detection and prevention cyberbullying considering the main characteristics of cyberbullying such as Intention to harm an individual, Repeatedly and over time and using abusive curl language or hate speech using supervised machine learning. The system relies on the detection of cyberbullying text along with the themes/categories associated with cyberbullying such as racist, sexual, physical mean, swear and other, using support vector machines and Logistic regression. The author of this research presents a new hypothesis for cyberbullying detection that the circumstances and usage of texting and its language have changed by time. Most of the studies have considered calling someone stupid, ugly and idiot as cyberbullying. Things have changed, such words may or may not always be a bullying incident. If a person wants intentionally to harm an individual, they will use extreme words. In addition to traditional feature extraction techniques like Term Frequency–Inverse Document Frequency (TF-IDF), N-gram and profanity along with sentiment analysis increases the accuracy of the system. Evaluated the proposed system using Recall, Precision and F1-score. [ABSTRACT FROM AUTHOR]

Nowadays, fake news has turned into a major problem because of the negative impact that it creates on society. Social media allows people to spread information on the internet with slight investigations and to add fewer filters than the actual content. Nowadays, the false news in the internet community is unsure, and it creates a wrong impression among the users. Detecting false news has become a critical task based on shared content. To tackle the false news growth in social media, various automatic detection schemes were evaluated. The "Natural Language Processing (NLP)" method also gives a prominent solution for false news detection. The main intention of this paper is to design and introduce an innovative false news recognition method using Meta-heuristic Searched-Ensemble Learning (MS-EL). Further, the selected features are extracted by the "Term Frequency-Inverse Document Frequency (TF-IDF)" and also Word2vec features. Here, the extracted selected features are integrated with the Hybrid Squirrel–Dragonfly Search Optimization (HS-DSO) is used to optimize the weighted feature selection approach with the fitness function of solving data variance and correlation. The proposed MS-EL is adopted in the classification part, having three sets of classifiers, Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Deep Neural Network (DNN). Here, the ensemble classifier is enhanced by the same HS-DSO that shows the parameter tuning with a high convergence rate. From the experimental outcomes, the accuracy of HS-DSO-MS-EL is 22% higher than BMO-MS-EL, 24% higher than SP-BMO-MS-EL, 30% higher than SSA-MS-EL, and 29% higher than DA-MS-EL. Thus, the experimental analysis with standard datasets establishes that the introduced fake news detection method has gained higher accuracy than the existing models. [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]