| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 97 === Due to the popularization of blogs, more and more enterprises attempt to acquire useful information from the huge amount of bloggers’ articles to discover customer needs, to trace market shift, and to assist them in improving quality of products or services. In addition, based on these discovered commercial knowledge, enterprises can analyze their strength and weakness compared with their competitors. Therefore, focusing on evaluation comments of products, this study proposes a FAIR (Fuzzy Adaptive resonance theory network based Information Retrieval) scheme by introducing fuzzy ART network, Latent Semantic Indexing (LSI), and Association Rules (AR) discovery to analyze evaluation comments of product features. In FAIR scheme, Fuzzy ART network first has been employed to segment bloggers. For each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, AR is presented to organize these keywords to form concepts. Finally, for further applications of these extracted voices of customers, Quality Function Deployment (QFD) and homogeneity analysis have been employed to transform customers needs to technical requirements and to understand the ability of competitors, respectively. Finally, a real case of cosmetics products has been provided to demonstrate the effectiveness of the proposed FAIR scheme.
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