Evaluation of a general-purpose sentiment lexicon on a product review corpus

Christopher S.G. Khoo; Sathik Basha Johnkhan; Jin Cheon Na

doi:10.1007/978-3-319-27974-9_9

Evaluation of a general-purpose sentiment lexicon on a product review corpus

Christopher S.G. Khoo^*, Sathik Basha Johnkhan, Jin Cheon Na

^*Corresponding author for this work

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This paper introduces a new general-purpose sentiment lexicon called the WKWSCI Sentiment Lexicon and compares it with three existing lexicons. The WKWSCI Sentiment Lexicon is based on the 6of12dict lexicon, and currently covers adjectives, adverbs and verbs. The words were manually coded with a value on a 7-point sentiment strength scale. The effectiveness of the four sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset. The WKWSCI lexicon obtained the best results for document-level sentiment categorization, with an accuracy of 75%. The Hu & Liu lexicon obtained the best results for sentence-level sentiment categorization, with an accuracy of 77%. The best bag-of-words machine learning model obtained an accuracy of 82% for document-level sentiment categorization model. The strength of the lexiconbased method is in sentence-level and aspect-based sentiment analysis, where it is difficult to apply machine-learning because of the small number of features.

Original language	English
Title of host publication	Digital Libraries
Subtitle of host publication	Providing Quality Information - 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Proceedings
Editors	Robert B. Allen, Jane Hunter, Marcia L. Zeng
Publisher	Springer Verlag
Pages	82-93
Number of pages	12
ISBN (Print)	9783319279732
DOIs	https://doi.org/10.1007/978-3-319-27974-9_9
Publication status	Published - 2015
Externally published	Yes
Event	17th International Conference on Digital Libraries: Providing Quality Information, ICADL 2015 - Seoul, Korea, Republic of Duration: Dec 9 2015 → Dec 12 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9469
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Digital Libraries: Providing Quality Information, ICADL 2015
Country/Territory	Korea, Republic of
City	Seoul
Period	12/9/15 → 12/12/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Sentiment analysis
Sentiment categorization
Sentiment lexicon

Access to Document

10.1007/978-3-319-27974-9_9

Cite this

Khoo, C. S. G., Johnkhan, S. B., & Na, J. C. (2015). Evaluation of a general-purpose sentiment lexicon on a product review corpus. In R. B. Allen, J. Hunter, & M. L. Zeng (Eds.), Digital Libraries: Providing Quality Information - 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Proceedings (pp. 82-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9469). Springer Verlag. https://doi.org/10.1007/978-3-319-27974-9_9

Khoo, Christopher S.G. ; Johnkhan, Sathik Basha ; Na, Jin Cheon. / Evaluation of a general-purpose sentiment lexicon on a product review corpus. Digital Libraries: Providing Quality Information - 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Proceedings. editor / Robert B. Allen ; Jane Hunter ; Marcia L. Zeng. Springer Verlag, 2015. pp. 82-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7568eda59a0c4852ba8b6da9039cd825,

title = "Evaluation of a general-purpose sentiment lexicon on a product review corpus",

abstract = "This paper introduces a new general-purpose sentiment lexicon called the WKWSCI Sentiment Lexicon and compares it with three existing lexicons. The WKWSCI Sentiment Lexicon is based on the 6of12dict lexicon, and currently covers adjectives, adverbs and verbs. The words were manually coded with a value on a 7-point sentiment strength scale. The effectiveness of the four sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset. The WKWSCI lexicon obtained the best results for document-level sentiment categorization, with an accuracy of 75%. The Hu & Liu lexicon obtained the best results for sentence-level sentiment categorization, with an accuracy of 77%. The best bag-of-words machine learning model obtained an accuracy of 82% for document-level sentiment categorization model. The strength of the lexiconbased method is in sentence-level and aspect-based sentiment analysis, where it is difficult to apply machine-learning because of the small number of features.",

keywords = "Sentiment analysis, Sentiment categorization, Sentiment lexicon",

author = "Khoo, {Christopher S.G.} and Johnkhan, {Sathik Basha} and Na, {Jin Cheon}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 17th International Conference on Digital Libraries: Providing Quality Information, ICADL 2015 ; Conference date: 09-12-2015 Through 12-12-2015",

year = "2015",

doi = "10.1007/978-3-319-27974-9_9",

language = "English",

isbn = "9783319279732",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Khoo, CSG, Johnkhan, SB & Na, JC 2015, Evaluation of a general-purpose sentiment lexicon on a product review corpus. in RB Allen, J Hunter & ML Zeng (eds), Digital Libraries: Providing Quality Information - 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9469, Springer Verlag, pp. 82-93, 17th International Conference on Digital Libraries: Providing Quality Information, ICADL 2015, Seoul, Korea, Republic of, 12/9/15. https://doi.org/10.1007/978-3-319-27974-9_9

Evaluation of a general-purpose sentiment lexicon on a product review corpus. / Khoo, Christopher S.G.; Johnkhan, Sathik Basha; Na, Jin Cheon.
Digital Libraries: Providing Quality Information - 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Proceedings. ed. / Robert B. Allen; Jane Hunter; Marcia L. Zeng. Springer Verlag, 2015. p. 82-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9469).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Evaluation of a general-purpose sentiment lexicon on a product review corpus

AU - Khoo, Christopher S.G.

AU - Johnkhan, Sathik Basha

AU - Na, Jin Cheon

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - This paper introduces a new general-purpose sentiment lexicon called the WKWSCI Sentiment Lexicon and compares it with three existing lexicons. The WKWSCI Sentiment Lexicon is based on the 6of12dict lexicon, and currently covers adjectives, adverbs and verbs. The words were manually coded with a value on a 7-point sentiment strength scale. The effectiveness of the four sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset. The WKWSCI lexicon obtained the best results for document-level sentiment categorization, with an accuracy of 75%. The Hu & Liu lexicon obtained the best results for sentence-level sentiment categorization, with an accuracy of 77%. The best bag-of-words machine learning model obtained an accuracy of 82% for document-level sentiment categorization model. The strength of the lexiconbased method is in sentence-level and aspect-based sentiment analysis, where it is difficult to apply machine-learning because of the small number of features.

AB - This paper introduces a new general-purpose sentiment lexicon called the WKWSCI Sentiment Lexicon and compares it with three existing lexicons. The WKWSCI Sentiment Lexicon is based on the 6of12dict lexicon, and currently covers adjectives, adverbs and verbs. The words were manually coded with a value on a 7-point sentiment strength scale. The effectiveness of the four sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset. The WKWSCI lexicon obtained the best results for document-level sentiment categorization, with an accuracy of 75%. The Hu & Liu lexicon obtained the best results for sentence-level sentiment categorization, with an accuracy of 77%. The best bag-of-words machine learning model obtained an accuracy of 82% for document-level sentiment categorization model. The strength of the lexiconbased method is in sentence-level and aspect-based sentiment analysis, where it is difficult to apply machine-learning because of the small number of features.

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KW - Sentiment categorization

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DO - 10.1007/978-3-319-27974-9_9

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SN - 9783319279732

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 82

EP - 93

BT - Digital Libraries

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A2 - Hunter, Jane

A2 - Zeng, Marcia L.

PB - Springer Verlag

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Y2 - 9 December 2015 through 12 December 2015

ER -

Khoo CSG, Johnkhan SB, Na JC. Evaluation of a general-purpose sentiment lexicon on a product review corpus. In Allen RB, Hunter J, Zeng ML, editors, Digital Libraries: Providing Quality Information - 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Proceedings. Springer Verlag. 2015. p. 82-93. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27974-9_9

Evaluation of a general-purpose sentiment lexicon on a product review corpus

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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