Journals / CMC / Vol.67, No.2
Table of Content

Research Article

BEST PAPER 2021

Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

Ameen Banjar1, Zohair Ahmed2, Ali Daud1, Rabeeh Ayaz Abbasi3, Hussain Dawood4,*
1 Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia
2 Air University, Islamabad, 44000, Pakistan
3 Department of Computer Science, Islamabad, 44000, Pakistan
4 Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21589 Saudi Arabia
* Corresponding Author: Hussain Dawood. Email:
(This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Abstract

Most consumers read online reviews written by different users before making purchase decisions, where each opinion expresses some sentiment. Therefore, sentiment analysis is currently a hot topic of research. In particular, aspect-based sentiment analysis concerns the exploration of emotions, opinions and facts that are expressed by people, usually in the form of polarity. It is crucial to consider polarity calculations and not simply categorize reviews as positive, negative, or neutral. Currently, the available lexicon-based method accuracy is affected by limited coverage. Several of the available polarity estimation techniques are too general and may not reflect the aspect/topic in question if reviews contain a wide range of information about different topics. This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis (ABSA-PER). ABSA-PER has three major phases: data preprocessing, aspect co-occurrence calculation (CAC) and polarity estimation. A multi-domain sentiment dataset, Twitter dataset, and trust pilot forum dataset (developed by us by defined judgement rules) are used to verify ABSA-PER. Experimental outcomes show that ABSA-PER achieves better accuracy, i.e., 85.7% accuracy for aspect extraction and 86.5% accuracy in terms of polarity estimation, than that of the baseline methods.

Keywords

Natural language processing; sentiment analysis; aspect co-occurrence calculation; sentiment polarity; customer reviews; twitter
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