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Sentiment Analysis using a Supervised Joint Topic Modelling Approach

Publication Type : Journal Article

Publisher : International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE)

Source : International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), Volume 8, Issue 7 (2017)

Campus : Chennai

School : School of Engineering

Department : Computer Science

Year : 2017

Abstract : Modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. The proposed model is a novel probabilistic supervised joint aspect and sentiment model (SJASM) to deal with the problems in one go under a unified framework. SJASM represents each review document in the form of opinion pairs, and can simultaneously model aspect terms and corresponding opinion words of the review for hidden aspect and sentiment detection. It also leverages sentimental overall ratings, which often come with online reviews, as supervision data, and can infer the semantic aspects and aspect-level sentiments that are not only meaningful but also predictive of overall sentiments of reviews. The efficient inference method is developed for parameter estimation of SJASM based on collapsed Gibbs sampling.

Cite this Research Publication : B. Natarajan, “Sentiment Analysis using a Supervised Joint Topic Modelling Approach” in International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE)., Volume 8, Issue 7 (2017)

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