On Modelling for Bias-Aware Sentiment Analysis and Its Impact in Twitter

  • Ahsan Mahmood Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan
  • Hikmat Ullah Khan Department of Computer Science COMSATS University Islamabad, Wah Campus, Pakistan
  • Muhammad Ramzan Department of Computer Science and IT, University of Sargodha, Sargodha, Pakistan
Keywords: Social Media, Twitter, Sentiment Analysis, Bias, data mining, opinion mining

Abstract

Sentiment Analysis (SA) is an active research area for the last ten years. SA is the computational treatment of opinions, sentiments, and subjectivity of text. Twitter is one of the most widely used micro-blog and considered as an important source for computation of sentiment and of data analysis. Therefore, companies all over the world analyze Twitter data using SA and extract knowledge which has potential applications in diverse areas. Although SA is the successful way of finding the people’s opinion, the bias in the tweets affects the results of the SA and reflects inaccurate analysis that may mislead users to take erroneous decisions. The biased tweets are shared by valid, but biased human users as well as the social bots to propagate the biased opinions on certain topics. To counter this, this research study proposes a statistical model to identify such users and social bots who share the biased content in the form of tweets in the Twitter social media. For experiment purpose, we use annotated twitter dataset and argue the results of SA with and without the biased tweets and explored the effects of biased users at micro-level and macro level. The empirical results show that the proposed approach is effective and properly identifies the biased users and bots from other authentic users using sentiment analysis.

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Author Biographies

Ahsan Mahmood, Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan

Ahsan Mahmood received the master’s degree in computer science from the COMSATS University, Attock campus, Pakistan. His research interests include Data Mining, Social Media Analysis, Sentiment Analysis and Machine Learning.

Hikmat Ullah Khan, Department of Computer Science COMSATS University Islamabad, Wah Campus, Pakistan

Hikmat Ullah Khan received the master’s degree in computer science and the Ph.D. degree in computer science from International Islamic University, Islamabad. He has been an Active Researcher for the last ten years. He is currently an Assistant Professor with the Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan. He has authored a number of research articles in top peer-reviewed journals and international conferences. His research interests include socialWeb mining, semanticWeb, data science, information retrieval, and scientometrics. He is an Editorial Board Member of a number of prestigious impact factor journals.

Muhammad Ramzan, Department of Computer Science and IT, University of Sargodha, Sargodha, Pakistan

Muhammad Ramzan is currently pursuing the Ph.D. degree with the University of Management and Technology, Lahore, Pakistan. He is currently a Lecturer with the University of Sargodha, Pakistan. He has authored several research articles published in reputed peer-reviewed journals. His areas of research include algorithms, machine learning, software engineering, and computer vision.

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Published
2020-03-05
Section
Articles