Sentiment Analysis on Twitter Data: Comparative Study on Different Approaches

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Abdur Rahman 1,* Mobashir Sadat 2 Saeed Siddik 2

1. Centre for Advanced Research in Sciences, University of Dhaka, Dhaka, Bangladesh

2. Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh

* Corresponding author.


Received: 8 Jan. 2021 / Revised: 15 Mar. 2021 / Accepted: 29 Apr. 2021 / Published: 8 Aug. 2021

Index Terms

Sentiment Analysis, Machine Learning, Twitter Data Comparative Analysis


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%.

Cite This Paper

Abdur Rahman, Mobashir Sadat, Saeed Siddik, "Sentiment Analysis on Twitter Data: Comparative Study on Different Approaches", International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.4, pp.1-13, 2021. DOI: 10.5815/ijisa.2021.04.01


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