A METHOD OF EXTRACTING AND EVALUATING GOOD AND BAD REPUTATIONS FOR NATURAL LANGUAGE EXPRESSIONS

Masao Fuketa, Yuki Kadoya, Elsayed Atlam (), Tsutomu Kunikata, Kazuhiro Morita, Shinkaku Kashiji and Jun-Ichi Aoe
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Masao Fuketa: Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, 770-8506, Japan
Yuki Kadoya: Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, 770-8506, Japan
Elsayed Atlam: Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, 770-8506, Japan
Tsutomu Kunikata: Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, 770-8506, Japan
Kazuhiro Morita: Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, 770-8506, Japan
Shinkaku Kashiji: Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, 770-8506, Japan
Jun-Ichi Aoe: Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, 770-8506, Japan

International Journal of Information Technology & Decision Making (IJITDM), 2005, vol. 04, issue 02, 177-196

Abstract: Although a users' opinion or a live voice is a very useful information for text mining of the business, it is difficult to extract good and bad reputations of users from texts written in natural language. The good and bad reputations discussed here depend on users' claims, interests and demands. This paper presents a method of determining these reputations in commodity review sentences. Multi-attribute rule is introduced to extract the reputations from sentences, and four-stage-rules are defined in order to evaluate good and bad reputations step by step. A deterministic multi-attribute pattern matching algorithm is utilized to determine the reputations efficiently.From simulation results for 2,240 review comments, it is verified that the multi-attribute pattern matching algorithm is 63.1 times faster than the Aho and Corasick method. The precision and recall of extracted reputations for each commodity are 94% and 93% respectively. Moreover, the precision and recall of the resulting reputations for each rule are 95% and 95% respectively.

Keywords: Good and bad reputations; text mining; natural language understanding; multi-attribute rules; deterministic multi-attribute pattern-matching (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1142/S0219622005001477

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