Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift

Sylvester Olubolu Orimaye; Saadat Mehmood Alhashmi; Eu-Gene Siew

doi:10.1007/978-3-642-32695-0_35

Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift

Sylvester Olubolu Orimaye, Saadat Mehmood Alhashmi, Eu-Gene Siew

Malaysia Sch of Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

8 Citations (Scopus)

Abstract

In recent years, sentiment classification has been an appealing task for so many reasons. However, the subtle manner in which people write reviews has made achieving high accuracy more challenging. In this paper, we investigate the improvements on sentiment classification baselines using sentiment polarity shift in reviews. We focus on Amazon online reviews for different types of product. First, we use our newly-proposed Sentence Polarity Shift (SPS) algorithm on review documents, reducing the relative classification loss due to inconsistent sentiment polarities within reviews by an average of 16% over a supervised sentiment classifier. Second, we build up on a popular supervised sentiment classification baseline by adding different features which provide better improvement over the original baseline. The improvement shown by this technique suggests modeling sentiment classification systems based on polarity shift combined with sentence and document-level features.

Original language	English
Title of host publication	PRICAI 2012
Subtitle of host publication	Trends in Artificial Intelligence - 12th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Editors	Patricia Anthony, Mitsuru Ishizuka, Dickson Lukose
Place of Publication	Berlin Germany
Publisher	Springer
Pages	386-399
Number of pages	14
ISBN (Print)	9783642326943
DOIs	https://doi.org/10.1007/978-3-642-32695-0_35
Publication status	Published - 2012
Event	Pacific Rim International Conference on Artificial Intelligence 2012 - Kuching, Malaysia Duration: 3 Sept 2012 → 7 Sept 2012 Conference number: 12th https://link.springer.com/book/10.1007/978-3-642-32695-0 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7458 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Pacific Rim International Conference on Artificial Intelligence 2012
Abbreviated title	PRICAI 2012
Country/Territory	Malaysia
City	Kuching
Period	3/09/12 → 7/09/12
Internet address	https://link.springer.com/book/10.1007/978-3-642-32695-0 (Proceedings)

Keywords

reviews
sentence polarity shift
Sentiment classification

Access to Document

10.1007/978-3-642-32695-0_35

Cite this

Orimaye, S. O., Alhashmi, S. M., & Siew, E.-G. (2012). Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift. In P. Anthony, M. Ishizuka, & D. Lukose (Eds.), PRICAI 2012: Trends in Artificial Intelligence - 12th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 386-399). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7458 LNAI). Springer. https://doi.org/10.1007/978-3-642-32695-0_35

Orimaye, Sylvester Olubolu ; Alhashmi, Saadat Mehmood ; Siew, Eu-Gene. / Buy it - Don't buy it : sentiment classification on amazon reviews using sentence polarity shift. PRICAI 2012: Trends in Artificial Intelligence - 12th Pacific Rim International Conference on Artificial Intelligence, Proceedings. editor / Patricia Anthony ; Mitsuru Ishizuka ; Dickson Lukose. Berlin Germany : Springer, 2012. pp. 386-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift",

abstract = "In recent years, sentiment classification has been an appealing task for so many reasons. However, the subtle manner in which people write reviews has made achieving high accuracy more challenging. In this paper, we investigate the improvements on sentiment classification baselines using sentiment polarity shift in reviews. We focus on Amazon online reviews for different types of product. First, we use our newly-proposed Sentence Polarity Shift (SPS) algorithm on review documents, reducing the relative classification loss due to inconsistent sentiment polarities within reviews by an average of 16\% over a supervised sentiment classifier. Second, we build up on a popular supervised sentiment classification baseline by adding different features which provide better improvement over the original baseline. The improvement shown by this technique suggests modeling sentiment classification systems based on polarity shift combined with sentence and document-level features.",

keywords = "reviews, sentence polarity shift, Sentiment classification",

author = "Orimaye, \{Sylvester Olubolu\} and Alhashmi, \{Saadat Mehmood\} and Eu-Gene Siew",

year = "2012",

doi = "10.1007/978-3-642-32695-0\_35",

language = "English",

isbn = "9783642326943",

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

publisher = "Springer",

pages = "386--399",

editor = "Patricia Anthony and Mitsuru Ishizuka and Dickson Lukose",

booktitle = "PRICAI 2012",

address = "Switzerland",

note = "Pacific Rim International Conference on Artificial Intelligence 2012, PRICAI 2012 ; Conference date: 03-09-2012 Through 07-09-2012",

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Orimaye, SO, Alhashmi, SM & Siew, E-G 2012, Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift. in P Anthony, M Ishizuka & D Lukose (eds), PRICAI 2012: Trends in Artificial Intelligence - 12th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7458 LNAI, Springer, Berlin Germany, pp. 386-399, Pacific Rim International Conference on Artificial Intelligence 2012, Kuching, Malaysia, 3/09/12. https://doi.org/10.1007/978-3-642-32695-0_35

Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift. / Orimaye, Sylvester Olubolu; Alhashmi, Saadat Mehmood; Siew, Eu-Gene.
PRICAI 2012: Trends in Artificial Intelligence - 12th Pacific Rim International Conference on Artificial Intelligence, Proceedings. ed. / Patricia Anthony; Mitsuru Ishizuka; Dickson Lukose. Berlin Germany: Springer, 2012. p. 386-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7458 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Buy it - Don't buy it

T2 - Pacific Rim International Conference on Artificial Intelligence 2012

AU - Orimaye, Sylvester Olubolu

AU - Alhashmi, Saadat Mehmood

AU - Siew, Eu-Gene

N1 - Conference code: 12th

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N2 - In recent years, sentiment classification has been an appealing task for so many reasons. However, the subtle manner in which people write reviews has made achieving high accuracy more challenging. In this paper, we investigate the improvements on sentiment classification baselines using sentiment polarity shift in reviews. We focus on Amazon online reviews for different types of product. First, we use our newly-proposed Sentence Polarity Shift (SPS) algorithm on review documents, reducing the relative classification loss due to inconsistent sentiment polarities within reviews by an average of 16% over a supervised sentiment classifier. Second, we build up on a popular supervised sentiment classification baseline by adding different features which provide better improvement over the original baseline. The improvement shown by this technique suggests modeling sentiment classification systems based on polarity shift combined with sentence and document-level features.

AB - In recent years, sentiment classification has been an appealing task for so many reasons. However, the subtle manner in which people write reviews has made achieving high accuracy more challenging. In this paper, we investigate the improvements on sentiment classification baselines using sentiment polarity shift in reviews. We focus on Amazon online reviews for different types of product. First, we use our newly-proposed Sentence Polarity Shift (SPS) algorithm on review documents, reducing the relative classification loss due to inconsistent sentiment polarities within reviews by an average of 16% over a supervised sentiment classifier. Second, we build up on a popular supervised sentiment classification baseline by adding different features which provide better improvement over the original baseline. The improvement shown by this technique suggests modeling sentiment classification systems based on polarity shift combined with sentence and document-level features.

KW - reviews

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DO - 10.1007/978-3-642-32695-0_35

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SN - 9783642326943

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

SP - 386

EP - 399

BT - PRICAI 2012

A2 - Anthony, Patricia

A2 - Ishizuka, Mitsuru

A2 - Lukose, Dickson

PB - Springer

CY - Berlin Germany

Y2 - 3 September 2012 through 7 September 2012

ER -

Orimaye SO, Alhashmi SM, Siew EG. Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift. In Anthony P, Ishizuka M, Lukose D, editors, PRICAI 2012: Trends in Artificial Intelligence - 12th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Berlin Germany: Springer. 2012. p. 386-399. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-32695-0_35

Buy it - Don't buy it: sentiment classification on amazon reviews using sentence polarity shift

Abstract

Publication series

Conference

Keywords

Access to Document

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Cite this