Article

Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques

Authors:

Tetsuya Nasukawa,

Razvan Bunescu,

Wayne NiblackAuthors Info & Claims

ICDM '03: Proceedings of the Third IEEE International Conference on Data Mining

Page 427

Published: 19 November 2003 Publication History

Abstract

We present Sentiment Analyzer (SA) that extracts sentiment(or opinion) about a subject from online text documents.Instead of classifying the sentiment of an entire documentabout a subject, SA detects all references to the givensubject, and determines sentiment in each of the referencesusing natural language processing (NLP) techniques. Oursentiment analysis consists of 1) a topic specific featureterm extraction, 2) sentiment extraction, and 3) (subject,sentiment) association by relationship analysis. SA utilizestwo linguistic resources for the analysis: the sentiment lexiconand the sentiment pattern database. The performanceof the algorithms was verified on online product review articles("digital camera" and "music" reviews), and moregeneral documents including general webpages and newsarticles.

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Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques

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cover image Guide Proceedings

ICDM '03: Proceedings of the Third IEEE International Conference on Data Mining

November 2003

ISBN:0769519784

Copyright © Copyright (c) 2003 Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

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IEEE Computer Society

United States

Publication History

Published: 19 November 2003

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Cited By

Lu KWu J(2019)Sentiment Analysis of Film Review Texts Based on Sentiment Dictionary and SVMProceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence10.1145/3319921.3319966(73-77)Online publication date: 15-Mar-2019
https://dl.acm.org/doi/10.1145/3319921.3319966
Liu YLi XWang M(2019)Quantifying Customer Review by Integrating Multiple Source of KnowledgeProceedings of the 2019 11th International Conference on Machine Learning and Computing10.1145/3318299.3318309(6-11)Online publication date: 22-Feb-2019
https://dl.acm.org/doi/10.1145/3318299.3318309
Djenouri DLaidi RDjenouri YBalasingham I(2019)Machine Learning for Smart Building ApplicationsACM Computing Surveys10.1145/331195052:2(1-36)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3311950
Lovinger JValova IClough C(2019)GistSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2882-223:5(1589-1601)Online publication date: 1-Mar-2019
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Gupta CGupta A(2018)A Collaborative Approach for Improvisation and Refinement of Requirement Prioritization ProcessJournal of Information Technology Research10.4018/JITR.201804010811:2(128-149)Online publication date: 1-Apr-2018
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Woltmann LThiele MLehner WDesai BFlesca SZumpano EMasciari ECaroprese L(2018)Modeling Customers and Products with Word Embeddings from Receipt DataProceedings of the 22nd International Database Engineering & Applications Symposium10.1145/3216122.3229860(246-252)Online publication date: 18-Jun-2018
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Touati IGraja MEllouze MBelguith L(2018)Opinion Target Extraction from Arabic News Articles Using shallow FeaturesProceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence10.1145/3177148.3180102(113-118)Online publication date: 27-Mar-2018
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Wang HRen J(2018)Research on the Validity of Online Commodity Reviews Based on Word2vecProceedings of the International Conference on Information Technology and Electrical Engineering 201810.1145/3148453.3306260(1-4)Online publication date: 7-Dec-2018
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Zhang SWei ZWang YLiao T(2018)Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionaryFuture Generation Computer Systems10.1016/j.future.2017.09.04881:C(395-403)Online publication date: 1-Apr-2018
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Karimi SShakery A(2017)A language-model-based approach for subjectivity detectionJournal of Information Science10.1177/016555151664181843:3(356-377)Online publication date: 1-Jun-2017
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