Muhammad Zubair Asghar

TL;DR: Lexicon-enhanced sentiment analysis based on Rule-based classification scheme is an alternative approach for improving sentiment classification of users’ reviews in online communities and the performance of the sentiment analysis is improved after considering emoticons, modifiers, negations, and domain specific terms when compared to baseline methods.

TL;DR: This review paper discusses existing techniques and approaches for feature extraction in sentiment analysis and opinion mining and adopted a systematic literature review process to identify areas well focused by researchers, least addressed areas are also highlighted giving an opportunity to researchers for further work.

TL;DR: Based on user-generated social media posts on Twitter, a tweet classification system is developed using deep learning-based sentiment analysis techniques to classify the tweets as extremist or non-extremist.

TL;DR: The findings revealed that the proposed method overcomes the limitations of previous methods by considering slang, emoticons, and domain‐specific terms.

TL;DR: This work investigates the rumor detection problem by exploring different Deep Learning models with emphasis on considering the contextual information in both directions: forward and backward, in a given text, effectively classifying the tweet into rumors and non-rumors.