Multi-aspects review summarization with objective information
Procedia - Social and Behavioral SciencesPublished 1 January 2011Open access
Kazutaka Shimada, Ryosuke Tadano, Tsutomu Endo
Citations20
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TL;DR
This paper proposes a method for multi-aspects review summarization based on evaluative sentence extraction that integrates the summary from sentiment information in reviews and the objective information extracted from Wikipedia.
Abstract
In this paper, we propose a method for multi-aspects review summarization based on evaluative sentence extraction. We handle three features; ratings of aspects, the tfidf value, and the number of mentions with a similar topic. For estimating the number of mentions, we apply a clustering algorithm. By using these features, we generate a more appropriate summary. In this paper, we also focus on objective information of the target product. We integrate the summary from sentiment information in reviews and the objective information extracted from Wikipedia. The experiment results show the effectiveness of our method.
Keywords
Computer Science
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