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Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach
2017
IEEE Transactions on Knowledge and Data Engineering
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
doi:10.1109/tkde.2017.2669027
fatcat:24uviz42ffhutc5ayjuvsudo4m