Sentiment Analysis for Online Reviews Using an Author-Review-Object Model
Lecture notes in computer sciencePublished 1 January 2011
Yong Zhang, Donghong Ji, Ying Su, Cheng Sun
Citations5
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
Preliminary experimental results show that the proposed model, called joint Author-Review-Object Model (ARO), is an effective strategy for jointly considering the various factors for the sentiment analysis.
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.
Keywords
Computer Science
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