Product information retrieval based on opinion mining

With the dramatically increase of E-commerce, product information gradually forms a massive scale. However, public search engine, such as Google, Baidu, can't satisfy user's need to search product information in recall ratio, especially accuracy ratio. As a result, the product information that user real needs can't be searched and acquired quickly and easily, so user spends lots of time in eliminating the useless information. Product opinion mining, namely analyze the user's opinion of products, can find out some of characteristics of the product itself and whether user is satisfied with the product characteristics. According to the description of product characteristics, we can draw some information which is fundamental to user's concern and used them for reliable recommendation in product information retrieval.

[1]  Q. Mudassar Ilyas,et al.  A conceptual architecture for semantic search engine , 2004, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[2]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[3]  Ramez Elmasri,et al.  Conceptual modeling and ontology extraction for web information , 2002 .

[4]  Chang Ouk Kim,et al.  Recommendation of e-commerce sites by matching category-based buyer query and product e-catalogs , 2008, Comput. Ind..

[5]  Clement T. Yu,et al.  Semantic-Based Grouping of Search Engine Results Using WordNet , 2007, APWeb/WAIM.

[6]  Jian Jiang Lexical semantic similarity and its application to business catalog retrieval , 1998 .

[7]  Lois M. L. Delcambre,et al.  Using semantic components to search for domain-specific documents: An evaluation from the system perspective and the user perspective , 2009, Inf. Syst..

[8]  Xia Lin Self-organizing semantic maps as graphical interfaces for information retrieval , 1993 .

[9]  Guy W. Mineau,et al.  Employing a Domain Specific Ontology to Perform Semantic Search , 2008, ICCS.

[10]  James Griffioen,et al.  A semantic object-oriented model for content-based retrieval , 1999 .

[11]  Yong Yu,et al.  Conceptual Graph Matching for Semantic Search , 2002, ICCS.

[12]  Hai Jin,et al.  Combining weights with fuzziness for intelligent semantic web search , 2008, Knowl. Based Syst..

[13]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[14]  Panagiotis G. Ipeirotis,et al.  Designing novel review ranking systems: predicting the usefulness and impact of reviews , 2007, ICEC.

[15]  Chin-Wan Chung,et al.  An effective semantic search technique using ontology , 2009, WWW '09.

[16]  Michael John Sussna,et al.  Text retrieval using inference in semantic metanetworks , 1997 .

[17]  Hai Jin,et al.  RSS: A framework enabling ranked search on the semantic web , 2008, Inf. Process. Manag..

[18]  Wooju Kim,et al.  Semantic Web Based Intelligent Product and Service Search Framework for Location-Based Services , 2005, ICCSA.

[19]  Thomas Y. Lee,et al.  Needs-based analysis of online customer reviews , 2007, ICEC.

[20]  Petya Osenova,et al.  Using a Domain-Ontology and Semantic Search in an E-Learning Environment , 2008, Innovative Techniques in Instruction Technology, E-learning, E-assessment, and Education.