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Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA

Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA

Nikhlesh Pathik, Pragya Shukla
Copyright: © 2022 |Volume: 24 |Issue: 3 |Pages: 19
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878223|DOI: 10.4018/JCIT.20220701.oa3
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MLA

Pathik, Nikhlesh, and Pragya Shukla. "Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA." JCIT vol.24, no.3 2022: pp.1-19. http://doi.org/10.4018/JCIT.20220701.oa3

APA

Pathik, N. & Shukla, P. (2022). Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA. Journal of Cases on Information Technology (JCIT), 24(3), 1-19. http://doi.org/10.4018/JCIT.20220701.oa3

Chicago

Pathik, Nikhlesh, and Pragya Shukla. "Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA," Journal of Cases on Information Technology (JCIT) 24, no.3: 1-19. http://doi.org/10.4018/JCIT.20220701.oa3

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Abstract

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.