Abstract
Online consumer reviews have become an essential source of information for understanding markets and customer preferences. This research introduces a novel topic model to identify product attributes and sentiments toward them at the sentence level. The model uses a recursive definition of topic distribution in a sentence to avoid the problem of over-parametrization in topic models. The introduction of the inference network enables the utilization of rich features in the content to drive the identification of sentiments, in contrast with other multi-aspect sentiment analysis models that rely on single words. The sentence topic model has a superior performance in producing coherent topics, and the sentence topic-sentiment model outperforms the existing model on the task of predicting product attribute rating.
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Chen, T., Parsons, J. (2018). A Sentence-Level Sparse Gamma Topic Model for Sentiment Analysis. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_33
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DOI: https://doi.org/10.1007/978-3-319-89656-4_33
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