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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.

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Abstract. Although E-mail systems are one of the most useful communication tools for business, education, etc,. It is very useful filtering supports for users to pick up important messages or to neglect unnecessary messages. This paper presents a method of determining the time priority for E-mail messages. Multi-attribute rules are defined to detect complex time expressions and a set pattern-matching machine is proposed. It enables us to protect missing messages with important time information because the presented method can classify and rank them according to time priority measurement automatically. From the simulation results of determining time priority, the presented pattern-matching method is from about 4 times faster than the traditional string pattern-matching method. From the results of filtering 5,172 sentences, precision and recall of the presented method becomes 95% and 96%, respectively. From the experimental results of determining 10 highest messages among 100 E-mail, filtering time is from 9.7 to 16.6 faster than that of a non-filtering method.