Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis

Abdullah, Nor Aniza and Feizollah, Ali and Ainin, Sulaiman and Anuar, Nor Badrul (2019) Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis. IEEE Access, 7. pp. 144957-144971. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2019.2945340

Abstract

The massive availability of online reviews and postings in social media offers invaluable feedback for businesses to make better informed decisions in steering their marketing strategies towards users' interests and preferences. Sentiment analysis is, therefore, essential for determining the public's opinion towards a particular topic, product or service. Traditionally, sentiment analysis is performed on a single data source, for instance, online product reviews or Tweets. However, the need to develop a more precise, and more comprehensive result has steered the move towards performing sentiment analysis on multiple data sources. The use of multiple data sources for a particular domain of interest can increase the amount of datasets needed for training a sentiment classifier. Till now, the problem of insufficient datasets for training the classifier is only addressed by multi-domain sentiment analysis. Aiming to equip researchers with a thorough understanding on both multi-source and multi-domain sentiment analysis, this paper aims to identify the underlying challenges of multi-source and multi-domain sentiment analysis, and discuss the solutions applied by the researchers concerned. This paper also offers an insightful discussion of the findings derived from past studies, and based on these, propose some useful suggestions for the future direction of this research area. Findings derived from our review would be beneficial towards guiding researchers towards the future progress and advancement of multi-source and multi-domain sentiment analysis. © 2013 IEEE.

Item Type: Article
Uncontrolled Keywords: multi-domain; multi-source; natural language processing; Sentiment analysis; transfer learning
Subjects: B Philosophy. Psychology. Religion > BP Islam. Bahaism. Theosophy, etc
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Deputy Vice Chancellor (Research & Innovation) Office
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 May 2020 04:52
Last Modified: 05 May 2020 04:52
URI: http://eprints.um.edu.my/id/eprint/24270

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