Multi-aspects review summarization with objective information | Zendy

Kazutaka Shimada | Zendy; Ryosuke Tadano | Zendy; Tsutomu Endo | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Multi-aspects review summarization with objective information

Author(s) -

Kazutaka Shimada,

Ryosuke Tadano,

Tsutomu Endo

Publication year - 2011

Publication title -

procedia - social and behavioral sciences

Language(s) - English

Resource type - Journals

ISSN - 1877-0428

DOI - 10.1016/j.sbspro.2011.10.592

Subject(s) - automatic summarization , computer science , tf–idf , information retrieval , focus (optics) , multi document summarization , cluster analysis , sentence , value (mathematics) , information extraction , data mining , artificial intelligence , natural language processing , machine learning , physics , quantum mechanics , term (time) , optics

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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research