Imran Razzak

TL;DR: This paper presents D I C E T, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account.

TL;DR: This study presents a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020 and supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic.

TL;DR: This work has developed a phishing detection approach that only needs nine lexical features for effectively detecting phishing attacks and has obtained the highest accuracy of 99.57% with the Random forest algorithm.

TL;DR: A framework called EDoT is proposed based on the research trends, common practices, and techniques used for detecting events on Twitter and can serve as a guideline for developing event detection methods, especially for researchers who are new in this area.

TL;DR: This paper analyzes twelve different pre- processing techniques on three pre-classified Twitter datasets on hate speech and proposes a systematic approach to text pre-processing to apply differentPre-processing techniques in order to retain features without information loss.