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Third IEEE International Conference on Data Mining (ICDM'03)
Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques
Melbourne, Florida
November 19-November 22
ISBN: 0-7695-1978-4
ASCII Text
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Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, Wayne Niblack, "Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques," Data Mining, IEEE International Conference on, pp. 427, Third IEEE International Conference on Data Mining (ICDM'03), 2003.
 
 
BibTex
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@article{ 10.1109/ICDM.2003.1250949,
author = {Jeonghee Yi and Tetsuya Nasukawa and Razvan Bunescu and Wayne Niblack},
title = {Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2003},
isbn = {0-7695-1978-4},
pages = {427},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250949},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques
SN - 0-7695-1978-4
SP
EP
A1 - Jeonghee Yi,
A1 - Tetsuya Nasukawa,
A1 - Razvan Bunescu,
A1 - Wayne Niblack,
PY - 2003
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Jeonghee Yi, IBM Almaden Research Center
Tetsuya Nasukawa, IBM Tokyo Research Lab
Razvan Bunescu, University of Texas, Austin
Wayne Niblack, IBM Almaden Research Center
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 document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general webpages and news articles.
Citation:
Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, Wayne Niblack, "Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques," icdm, pp.427, Third IEEE International Conference on Data Mining (ICDM'03), 2003
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