Mobashir Sadat

Work place: Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh



Research Interests: Natural Language Processing


Mobashir Sadat graduated with a BSc in Software Engineering from University of Dhaka. His research interests lie in the areas of natural language processing, machine learning and information retrieval. Specifically, he is interested in sentiment analysis, text summarization, natural language inference and hierarchical topic classification. Currently, he is a computer science PhD student at University of Illinois at Chicago.

Author Articles
Sentiment Analysis on Twitter Data: Comparative Study on Different Approaches

By Abdur Rahman Mobashir Sadat Saeed Siddik

DOI:, Pub. Date: 8 Aug. 2021

Social media has become incredibly popular these days for communicating with friends and for sharing opinions. According to current statistics, almost 2.22 billion people use social media in 2016, which is roughly one third of the world population and three times of the entire population in Europe. In social media people share their likes, dislikes, opinions, interests, etc. so it is possible to know about a person’s thoughts about a specific topic from the shared data in social media. Since, twitter is one of the most popular social media in the world; it is a very good source for opinion mining and sentiment analysis about different topics. In this research, SVM with different kernel functions and Adaboost are experimented using CPD and Chi-square feature extraction techniques to explore the best sentiment classification model. The reported average accuracy of Adaboost for Chi-square and CPD are 70.2% and 66.9%. The SVM radial basis kernel and polynomial kernel with Chi-square n-grams reported average accuracy of 73.73% and 68.67% respectively. Among the performed experimentation, SVM sigmoid kernel with Chi-square n-grams provided the maximum accuracy that is 74.4%.

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