Persistent Link:
http://hdl.handle.net/10150/106241
Title:
A sentiment-based meta search engine
Author:
Na, Jin-Cheon; Khoo, Christopher S.G.; Chan, Syin
Editors:
Khoo, C.; Singh, D.; Chaudhry, A.S.
Citation:
A sentiment-based meta search engine 2006, :83-89
Publisher:
School of Communication & Information, Nanyang Technological University
Issue Date:
2006
URI:
http://hdl.handle.net/10150/106241
Submitted date:
2007-05-22
Abstract:
This study is in the area of sentiment classification: classifying online review documents according to the overall sentiment expressed in them. This paper presents a prototype sentiment-based meta search engine that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended or non-recommended information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents. It does this by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM). This paper also discusses various issues we have encountered during the prototype development, and presents our approaches for resolving them. A user evaluation of the prototype was carried out with positive responses from users.
Type:
Conference Paper
Language:
en
Keywords:
Classification; Web Mining; Information Retrieval; Natural Language Processing
Local subject classification:
Sentiment classification; Meta search engine

Full metadata record

DC FieldValue Language
dc.contributor.authorNa, Jin-Cheonen_US
dc.contributor.authorKhoo, Christopher S.G.en_US
dc.contributor.authorChan, Syinen_US
dc.contributor.editorKhoo, C.en_US
dc.contributor.editorSingh, D.en_US
dc.contributor.editorChaudhry, A.S.en_US
dc.date.accessioned2007-05-22T00:00:01Z-
dc.date.available2010-06-18T23:43:06Z-
dc.date.issued2006en_US
dc.date.submitted2007-05-22en_US
dc.identifier.citationA sentiment-based meta search engine 2006, :83-89en_US
dc.identifier.urihttp://hdl.handle.net/10150/106241-
dc.description.abstractThis study is in the area of sentiment classification: classifying online review documents according to the overall sentiment expressed in them. This paper presents a prototype sentiment-based meta search engine that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended or non-recommended information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents. It does this by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM). This paper also discusses various issues we have encountered during the prototype development, and presents our approaches for resolving them. A user evaluation of the prototype was carried out with positive responses from users.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherSchool of Communication & Information, Nanyang Technological Universityen_US
dc.subjectClassificationen_US
dc.subjectWeb Miningen_US
dc.subjectInformation Retrievalen_US
dc.subjectNatural Language Processingen_US
dc.subject.otherSentiment classificationen_US
dc.subject.otherMeta search engineen_US
dc.titleA sentiment-based meta search engineen_US
dc.typeConference Paperen_US
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