Sentiment classification on polarity reviews: an empirical study using rating-based features

Dai Quoc Nguyen; Dat Quoc Nguyen; Thanh Vu; Son Bao Pham

Sentiment classification on polarity reviews: an empirical study using rating-based features

Dai Quoc Nguyen, Dat Quoc Nguyen, Thanh Vu, Son Bao Pham

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

Abstract

We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task.

Original language	English
Title of host publication	ACL 2014
Subtitle of host publication	5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: proceedings of the workshop
Place of Publication	Pennsylvania
Publisher	Association for Computational Linguistics (ACL)
Pages	128-135
Number of pages	8
ISBN (Print)	9781941643112
Publication status	Published - 1 Jun 2014
Externally published	Yes

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http://www.aclweb.org/anthology/W14-2621

Cite this

Nguyen, D. Q., Nguyen, D. Q., Vu, T., & Pham, S. B. (2014). Sentiment classification on polarity reviews: an empirical study using rating-based features. In ACL 2014: 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: proceedings of the workshop (pp. 128-135). Association for Computational Linguistics (ACL). http://www.aclweb.org/anthology/W14-2621

@inproceedings{01973ac52a6f4de0bcb79b525982e8e6,

title = "Sentiment classification on polarity reviews: an empirical study using rating-based features",

abstract = "We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task.",

author = "Nguyen, {Dai Quoc} and Nguyen, {Dat Quoc} and Thanh Vu and Pham, {Son Bao}",

year = "2014",

month = jun,

day = "1",

language = "English",

isbn = "9781941643112",

pages = "128--135",

booktitle = "ACL 2014",

publisher = "Association for Computational Linguistics (ACL)",

}

Nguyen, DQ, Nguyen, DQ, Vu, T & Pham, SB 2014, Sentiment classification on polarity reviews: an empirical study using rating-based features. in ACL 2014: 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: proceedings of the workshop. Association for Computational Linguistics (ACL), Pennsylvania, pp. 128-135. <http://www.aclweb.org/anthology/W14-2621>

Sentiment classification on polarity reviews : an empirical study using rating-based features. / Nguyen, Dai Quoc; Nguyen, Dat Quoc; Vu, Thanh et al.

ACL 2014: 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: proceedings of the workshop. Pennsylvania : Association for Computational Linguistics (ACL), 2014. p. 128-135.

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

TY - GEN

T1 - Sentiment classification on polarity reviews

T2 - an empirical study using rating-based features

AU - Nguyen, Dai Quoc

AU - Nguyen, Dat Quoc

AU - Vu, Thanh

AU - Pham, Son Bao

PY - 2014/6/1

Y1 - 2014/6/1

N2 - We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task.

AB - We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task.

M3 - Conference proceeding contribution

SN - 9781941643112

SP - 128

EP - 135

BT - ACL 2014

PB - Association for Computational Linguistics (ACL)

CY - Pennsylvania

ER -

Sentiment classification on polarity reviews: an empirical study using rating-based features

Abstract

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