Mining semantic patterns for sentiment analysis of product reviews

Sang Sang Tan; Jin Cheon Na

doi:10.1007/978-3-319-67008-9_30

Mining semantic patterns for sentiment analysis of product reviews

Sang Sang Tan^*, Jin Cheon Na

^*Corresponding author for this work

Nanyang Technological University

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

11 Citations (Scopus)

Abstract

A central challenge in building sentiment classifiers using machine learning approach is the generation of discriminative features that allow sentiment to be implied. Researchers have made significant progress with various features such as n-grams, sentiment shifters, and lexicon features. However, the potential of semantics-based features in sentiment classification has not been fully explored. By integrating PropBank-based semantic parsing and class association rule (CAR) mining, this study aims to mine patterns of semantic labels from domain corpus for sentence-level sentiment analysis of product reviews. With the features generated from the semantic patterns, the F-score of the sentiment classifier was boosted to 82.31% at minimum confidence level of 0.75, which not only indicated a statistically significant improvement over the baseline classifier with unigram and negation features (F-score = 73.93%) but also surpassed the best performance obtained with other classifiers trained on generic lexicon features (F-score = 76.25%) and domain-specific lexicon features (F-score = 78.91%).

Original language	English
Title of host publication	Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Proceedings
Editors	Yannis Manolopoulos, Jaap Kamps, Giannis Tsakonas, Lazaros Iliadis, Ioannis Karydis
Publisher	Springer Verlag
Pages	382-393
Number of pages	12
ISBN (Print)	9783319670072
DOIs	https://doi.org/10.1007/978-3-319-67008-9_30
Publication status	Published - 2017
Externally published	Yes
Event	21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017 - Thessaloniki, Greece Duration: Sept 18 2017 → Sept 21 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10450 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017
Country/Territory	Greece
City	Thessaloniki
Period	9/18/17 → 9/21/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Machine learning
Pattern mining
Semantic parsing
Sentiment analysis
Sentiment classification

Access to Document

10.1007/978-3-319-67008-9_30

Cite this

Tan, S. S., & Na, J. C. (2017). Mining semantic patterns for sentiment analysis of product reviews. In Y. Manolopoulos, J. Kamps, G. Tsakonas, L. Iliadis, & I. Karydis (Eds.), Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Proceedings (pp. 382-393). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10450 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67008-9_30

Tan, Sang Sang ; Na, Jin Cheon. / Mining semantic patterns for sentiment analysis of product reviews. Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Proceedings. editor / Yannis Manolopoulos ; Jaap Kamps ; Giannis Tsakonas ; Lazaros Iliadis ; Ioannis Karydis. Springer Verlag, 2017. pp. 382-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A central challenge in building sentiment classifiers using machine learning approach is the generation of discriminative features that allow sentiment to be implied. Researchers have made significant progress with various features such as n-grams, sentiment shifters, and lexicon features. However, the potential of semantics-based features in sentiment classification has not been fully explored. By integrating PropBank-based semantic parsing and class association rule (CAR) mining, this study aims to mine patterns of semantic labels from domain corpus for sentence-level sentiment analysis of product reviews. With the features generated from the semantic patterns, the F-score of the sentiment classifier was boosted to 82.31\% at minimum confidence level of 0.75, which not only indicated a statistically significant improvement over the baseline classifier with unigram and negation features (F-score = 73.93\%) but also surpassed the best performance obtained with other classifiers trained on generic lexicon features (F-score = 76.25\%) and domain-specific lexicon features (F-score = 78.91\%).",

keywords = "Machine learning, Pattern mining, Semantic parsing, Sentiment analysis, Sentiment classification",

author = "Tan, \{Sang Sang\} and Na, \{Jin Cheon\}",

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Tan, SS & Na, JC 2017, Mining semantic patterns for sentiment analysis of product reviews. in Y Manolopoulos, J Kamps, G Tsakonas, L Iliadis & I Karydis (eds), Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10450 LNCS, Springer Verlag, pp. 382-393, 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, 9/18/17. https://doi.org/10.1007/978-3-319-67008-9_30

Mining semantic patterns for sentiment analysis of product reviews. / Tan, Sang Sang; Na, Jin Cheon.
Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Proceedings. ed. / Yannis Manolopoulos; Jaap Kamps; Giannis Tsakonas; Lazaros Iliadis; Ioannis Karydis. Springer Verlag, 2017. p. 382-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10450 LNCS).

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

TY - GEN

T1 - Mining semantic patterns for sentiment analysis of product reviews

AU - Tan, Sang Sang

AU - Na, Jin Cheon

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - A central challenge in building sentiment classifiers using machine learning approach is the generation of discriminative features that allow sentiment to be implied. Researchers have made significant progress with various features such as n-grams, sentiment shifters, and lexicon features. However, the potential of semantics-based features in sentiment classification has not been fully explored. By integrating PropBank-based semantic parsing and class association rule (CAR) mining, this study aims to mine patterns of semantic labels from domain corpus for sentence-level sentiment analysis of product reviews. With the features generated from the semantic patterns, the F-score of the sentiment classifier was boosted to 82.31% at minimum confidence level of 0.75, which not only indicated a statistically significant improvement over the baseline classifier with unigram and negation features (F-score = 73.93%) but also surpassed the best performance obtained with other classifiers trained on generic lexicon features (F-score = 76.25%) and domain-specific lexicon features (F-score = 78.91%).

AB - A central challenge in building sentiment classifiers using machine learning approach is the generation of discriminative features that allow sentiment to be implied. Researchers have made significant progress with various features such as n-grams, sentiment shifters, and lexicon features. However, the potential of semantics-based features in sentiment classification has not been fully explored. By integrating PropBank-based semantic parsing and class association rule (CAR) mining, this study aims to mine patterns of semantic labels from domain corpus for sentence-level sentiment analysis of product reviews. With the features generated from the semantic patterns, the F-score of the sentiment classifier was boosted to 82.31% at minimum confidence level of 0.75, which not only indicated a statistically significant improvement over the baseline classifier with unigram and negation features (F-score = 73.93%) but also surpassed the best performance obtained with other classifiers trained on generic lexicon features (F-score = 76.25%) and domain-specific lexicon features (F-score = 78.91%).

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KW - Sentiment classification

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

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EP - 393

BT - Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Proceedings

A2 - Manolopoulos, Yannis

A2 - Kamps, Jaap

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Tan SS, Na JC. Mining semantic patterns for sentiment analysis of product reviews. In Manolopoulos Y, Kamps J, Tsakonas G, Iliadis L, Karydis I, editors, Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Proceedings. Springer Verlag. 2017. p. 382-393. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67008-9_30

Mining semantic patterns for sentiment analysis of product reviews

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this