Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach | IEEE Journals & Magazine | IEEE Xplore

Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach


Abstract:

In this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as we...Show More

Abstract:

In this work, we focus on 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. We propose 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. Moreover, we also develop efficient inference method for parameter estimation of SJASM based on collapsed Gibbs sampling. We evaluate SJASM extensively on real-world review data, and experimental results demonstrate that the proposed model outperforms seven well-established baseline methods for sentiment analysis tasks.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 29, Issue: 6, 01 June 2017)
Page(s): 1172 - 1185
Date of Publication: 14 February 2017

ISSN Information:

Funding Agency:


References

References is not available for this document.