Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis

Sang Sang Tan; Jin Cheon Na

doi:10.1007/978-3-319-49304-6_34

Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis

Sang Sang Tan^*, Jin Cheon Na

^*Corresponding author for this work

Nanyang Technological University

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

3 Citations (Scopus)

Abstract

The increasing interest to extract valuable information from networked data has heightened the need for effective and reliable sentiment analysis techniques. To this end, lexicon-based sentiment classification has been extensively studied by the research community. However, little is known about the usefulness of different multi-word constructs in creating domain-specific sentiment lexicons. Thus, our primary objective in this paper is to evaluate the performance of bigram, typed dependency, and concept as multi-word lexical entries for domain-specific sentiment classification. Pointwise Mutual Information (PMI) was adopted to select the lexical entries and to calculate the sentiment scores of the multi-word terms. With the features generated from the domain lexicons, a series of experiments were carried out using support vector machine (SVM) classifiers. While all the domain-specific classifiers outperformed the baseline classifier, our results showed that lexicons consisting of bigram entries and typed dependency entries improved the performance to a greater extent.

Original language	English
Title of host publication	Digital Libraries
Subtitle of host publication	Knowledge, Information, and Data in an Open Access Society - 18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016, Proceedings
Editors	Atsuyuki Morishima, Andreas Rauber, Chern li Liew
Publisher	Springer Verlag
Pages	285-296
Number of pages	12
ISBN (Print)	9783319493039
DOIs	https://doi.org/10.1007/978-3-319-49304-6_34
Publication status	Published - 2016
Externally published	Yes
Event	18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016 - Tsukuba, Japan Duration: Dec 7 2016 → Dec 9 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10075 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016
Country/Territory	Japan
City	Tsukuba
Period	12/7/16 → 12/9/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Machine learning
Sentiment analysis
Sentiment classification
Sentiment lexicon

Access to Document

10.1007/978-3-319-49304-6_34

Cite this

Tan, S. S., & Na, J. C. (2016). Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis. In A. Morishima, A. Rauber, & C. li Liew (Eds.), Digital Libraries: Knowledge, Information, and Data in an Open Access Society - 18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016, Proceedings (pp. 285-296). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10075 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-49304-6_34

Tan, Sang Sang ; Na, Jin Cheon. / Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis. Digital Libraries: Knowledge, Information, and Data in an Open Access Society - 18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016, Proceedings. editor / Atsuyuki Morishima ; Andreas Rauber ; Chern li Liew. Springer Verlag, 2016. pp. 285-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The increasing interest to extract valuable information from networked data has heightened the need for effective and reliable sentiment analysis techniques. To this end, lexicon-based sentiment classification has been extensively studied by the research community. However, little is known about the usefulness of different multi-word constructs in creating domain-specific sentiment lexicons. Thus, our primary objective in this paper is to evaluate the performance of bigram, typed dependency, and concept as multi-word lexical entries for domain-specific sentiment classification. Pointwise Mutual Information (PMI) was adopted to select the lexical entries and to calculate the sentiment scores of the multi-word terms. With the features generated from the domain lexicons, a series of experiments were carried out using support vector machine (SVM) classifiers. While all the domain-specific classifiers outperformed the baseline classifier, our results showed that lexicons consisting of bigram entries and typed dependency entries improved the performance to a greater extent.",

keywords = "Machine learning, Sentiment analysis, Sentiment classification, Sentiment lexicon",

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Tan, SS & Na, JC 2016, Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis. in A Morishima, A Rauber & C li Liew (eds), Digital Libraries: Knowledge, Information, and Data in an Open Access Society - 18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10075 LNCS, Springer Verlag, pp. 285-296, 18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016, Tsukuba, Japan, 12/7/16. https://doi.org/10.1007/978-3-319-49304-6_34

Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis. / Tan, Sang Sang; Na, Jin Cheon.
Digital Libraries: Knowledge, Information, and Data in an Open Access Society - 18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016, Proceedings. ed. / Atsuyuki Morishima; Andreas Rauber; Chern li Liew. Springer Verlag, 2016. p. 285-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10075 LNCS).

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

TY - GEN

T1 - Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis

AU - Tan, Sang Sang

AU - Na, Jin Cheon

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - The increasing interest to extract valuable information from networked data has heightened the need for effective and reliable sentiment analysis techniques. To this end, lexicon-based sentiment classification has been extensively studied by the research community. However, little is known about the usefulness of different multi-word constructs in creating domain-specific sentiment lexicons. Thus, our primary objective in this paper is to evaluate the performance of bigram, typed dependency, and concept as multi-word lexical entries for domain-specific sentiment classification. Pointwise Mutual Information (PMI) was adopted to select the lexical entries and to calculate the sentiment scores of the multi-word terms. With the features generated from the domain lexicons, a series of experiments were carried out using support vector machine (SVM) classifiers. While all the domain-specific classifiers outperformed the baseline classifier, our results showed that lexicons consisting of bigram entries and typed dependency entries improved the performance to a greater extent.

AB - The increasing interest to extract valuable information from networked data has heightened the need for effective and reliable sentiment analysis techniques. To this end, lexicon-based sentiment classification has been extensively studied by the research community. However, little is known about the usefulness of different multi-word constructs in creating domain-specific sentiment lexicons. Thus, our primary objective in this paper is to evaluate the performance of bigram, typed dependency, and concept as multi-word lexical entries for domain-specific sentiment classification. Pointwise Mutual Information (PMI) was adopted to select the lexical entries and to calculate the sentiment scores of the multi-word terms. With the features generated from the domain lexicons, a series of experiments were carried out using support vector machine (SVM) classifiers. While all the domain-specific classifiers outperformed the baseline classifier, our results showed that lexicons consisting of bigram entries and typed dependency entries improved the performance to a greater extent.

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ER -

Tan SS, Na JC. Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis. In Morishima A, Rauber A, li Liew C, editors, Digital Libraries: Knowledge, Information, and Data in an Open Access Society - 18th International Conference on Asia-Pacific Digital Libraries, ICADL 2016, Proceedings. Springer Verlag. 2016. p. 285-296. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-49304-6_34

Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this