2007 FilteringProductReviewsFromWebSearchResults
- (Thet et al., 2007) ⇒ Tun Thura Thet, Jin-Cheon Na, and Christopher S. G. Khoo. (2007). “Filtering Product Reviews from Web Search Results.” In: Proceedings of the 2007 ACM symposium on Document Engineering.
Subject Headings: Text Categorization Algorithm, Product Review Classification Task
Notes
- It compares the performance of a Supervised Learning Algorithm and a Heuristic Approach to a Text Categorization Task that is based on Search Snippets.
- The Search Snippets are from Google queries using the format “[product name] review”.
- http://www.springerlink.com/content/85p31628j25r7505/
Cited By
Quotes
Abstract
This study seeks to develop an automatic method to identify product reviews on the Web using the snippets (summary information) returned by search engines. Determining whether a snippet is a review or non-review is a challenging task, since the snippet usually does not contain many useful features for identifying review documents. Firstly we applied a common machine learning technique, SVM (Support Vector Machine), to investigate which features of snippets are useful for the classification. Then we employed a heuristic approach utilizing domain knowledge and found that the heuristic approach performs equally well as the machine learning approach. A hybrid approach which combines the machine learning technique and domain knowledge performs slightly better than the machine learning approach alone.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2007 FilteringProductReviewsFromWebSearchResults | Tun Thura Thet Jin-Cheon Na Christopher S. G. Khoo | Filtering Product Reviews from Web Search Results | Proceedings of the 2007 ACM symposium on Document Engineering | 10.1145/1284420.1284467 | 2007 |