Abstract
In this paper, we propose a probabilistic generative model for online review sentiment analysis, called joint Author-Review-Object Model (ARO). The users, objects and reviews form a heterogeneous graph in online reviews. The ARO model focuses on utilizing the user-review-object graph to improve the traditional sentiment analysis. It detects the sentiment based on not only the review content but also the author and object information. Preliminary experimental results on three datasets show that the proposed model is an effective strategy for jointly considering the various factors for the sentiment analysis.
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Zhang, Y., Ji, DH., Su, Y., Sun, C. (2011). Sentiment Analysis for Online Reviews Using an Author-Review-Object Model. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_33
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DOI: https://doi.org/10.1007/978-3-642-25631-8_33
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