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A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges

Atika Qazi (Department of Artificial Intelligence, University of Malaya, Kuala Lumpur, Malaysia) (Centre for Life Long Learning, University Brunei Darussalam, Gadong, Brunei Darussalam) (Faculty of Computer Science and Information Technology, COMSATS Institute of Information Technology, Islamabad, Pakistan)
Ram Gopal Raj (Department of Artificial Intelligence, University of Malaya, Kuala Lumpur, Malaysia)
Glenn Hardaker (Centre for Lifelong Learning and Sultan Hassanal Bolkiah Institute of Education, University Brunei Darussalam, Gadong, Brunei Darussalam)
Craig Standing (Centre for Innovative Practice, Edith Cowan University, Perth, Australia)

Internet Research

ISSN: 1066-2243

Article publication date: 5 June 2017




The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.


A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.


The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

Research limitations/implications

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.


Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.



This project is supported by IPPP Research Grant RP026-14AET from University Malaya, Malaysia. The authors are also thankful to BRC-12 by University Brunei Darussalam, Brunei, for their support in this research.


Qazi, A., Raj, R.G., Hardaker, G. and Standing, C. (2017), "A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges", Internet Research, Vol. 27 No. 3, pp. 608-630.



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