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


Authors: Z. Hai, G. Cong, K. Chang, P. Cheng, and C. Miao
Title: 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 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 comes 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.
Keywords: Sentiment analysis; Aspect-based sentiment analysis; Probabilistic topic model; Supervised joint topic model
Journal Name: IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 6
Publisher: IEEE
Year: 2017
Accepted PDF File: Analyzing_Sentiments_in_One_Go_A_Supervised_Joint_Topic_Modeling_Approach_accepted.pdf
Permanent Link: https://doi.org/10.1109/TKDE.2017.2669027
Reference: Z. Hai, G. Cong, K. Chang, P. Cheng, and C. Miao, “Analyzing sentiments in one go: A supervised joint topic modeling approach,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 6, pp. 1172–1185, June 2017.
bibtex: 
@article {LILY-j50,
    author 	= {Hai, Zhen and Cong, Gao and Chang, Kuiyu and Cheng, Peng and Miao, Chunyan},
    title 	= {Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach},
    journal 	= {IEEE Transactions on Knowledge and Data Engineering},
    year 	= {2017},
    month 	= {June},
    volume 	= {29},
    number 	= {6},
    pages 	= {1172-1185},
    publisher 	= {IEEE},
 }