With the rapid expansion of e-commerce, the Web has become an excellent source for
gathering customer opinions (or so-called customer reviews). Customer reviews are
essential for merchants or product manufacturers to understand general responses of
customers on their products for product or marketing campaign improvement. In
addition, customer reviews can enable merchants better understand specific
preferences of individual customers and facilitates making effective marketing
decisions. Prior data mining research mainly concentrates on analyzing customer
demographic, attitudinal, psychographic, transactional, and behavioral data for
supporting customer relationship management and marketing decision making and did
not pay attention to the use of customer reviews as additional source for marketing
intelligence. Thus, the purpose of this research is to develop an efficient and effective
opinion summarization technique. Specifically, we will propose a semantic-based
product feature extraction technique (SPE) which aims at improving the existing
product feature extraction technique and is desired to enhance the overall opinion
summarization effectiveness.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0720106-142036 |
| Date | 20 July 2006 |
| Creators | Chen, Yen-Ming |
| Contributors | none, Christopher Yang, Chih-Ping Wei |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0720106-142036 |
| Rights | not_available, Copyright information available at source archive |
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