Abusive Text Detection Using Neural Networks

eural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, the conclusions are: (1) Extracting selected manual features can increase abusive content detection over using basic ngrams; (2) Although averaging pre-trained word embeddings is a naive method, the distributed feature representation has better performance to ngrams in most of our datasets; (3) While the FastText classifier works efficiently with fast performance, the results are not remarkable as it is a shallow neural network with only one hidden layer; (4) Using pre-trained word embeddings does not guarantee better performance in the FastText classifie

Abusive Text Detection Using Neural Networks

Neurall network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architecture has recently attracted a high level of attention amongst researchers

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Context-Aware Sentiment Analysis using Tweet Expansion Method

The large source of information space produced by the plethora of social media platforms in general and microblogging in particular has spawned a slew of new applications and prompted the rise and expansion of sentiment analysis research. We propose a sentiment analysis technique that identifies the main parts to describe tweet intent and also enriches them with relevant words, phrases, or even inferred variables. We followed a state-of-the-art hybrid deep learning model to combine Convolutional Neural Network (CNN) and the Long Short-Term Memory network (LSTM) to classify tweet data based on their polarity. To preserve the latent relationships between tweet terms and their expanded representation, sentence encoding and contextualized word embeddings are utilized. To investigate the performance of tweet embeddings on the sentiment analysis task, we tested several context-free models (Word2Vec, Sentence2Vec, Glove, and FastText), a dynamic embedding model (BERT), deep contextualized word representations (ELMo), and an entity-based model (Wikipedia). The proposed method and results prove that text enrichment improves the accuracy of sentiment polarity classification with a notable percentage