Improving sentiment classification using feature highlighting and feature bagging

Lin Dai; Hechun Chen; Xuemei Li

doi:10.1109/ICDMW.2011.96

Improving sentiment classification using feature highlighting and feature bagging

Lin Dai^*, Hechun Chen, Xuemei Li

^*Corresponding author for this work

School of Computer Science and Technology

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

8 Citations (Scopus)

Abstract

Sentiment classification is an important data mining task. Previous researches tried various machine learning techniques while didn't make fully use of the difference among features. This paper proposes a novel method for improving sentiment classification by fully exploring the different contribution of features. The method consists of two parts. First, we highlight sentimental features by increasing their weight. Second, we use bagging to construct multiple classifiers on different feature spaces and combine them into an aggregating classifier. Extensive experiments show that the method can evidently improve the performance of sentiment classification.

Original language	English
Title of host publication	Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages	61-66
Number of pages	6
DOIs	https://doi.org/10.1109/ICDMW.2011.96
Publication status	Published - 2011
Event	11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada Duration: 11 Dec 2011 → 11 Dec 2011

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Conference

Conference	11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Country/Territory	Canada
City	Vancouver, BC
Period	11/12/11 → 11/12/11

Keywords

Feature bagging
Feature highlighting
Sentiment analysis

Access to Document

10.1109/ICDMW.2011.96

Cite this

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title = "Improving sentiment classification using feature highlighting and feature bagging",

abstract = "Sentiment classification is an important data mining task. Previous researches tried various machine learning techniques while didn't make fully use of the difference among features. This paper proposes a novel method for improving sentiment classification by fully exploring the different contribution of features. The method consists of two parts. First, we highlight sentimental features by increasing their weight. Second, we use bagging to construct multiple classifiers on different feature spaces and combine them into an aggregating classifier. Extensive experiments show that the method can evidently improve the performance of sentiment classification.",

keywords = "Feature bagging, Feature highlighting, Sentiment analysis",

author = "Lin Dai and Hechun Chen and Xuemei Li",

year = "2011",

doi = "10.1109/ICDMW.2011.96",

language = "English",

isbn = "9780769544090",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

pages = "61--66",

booktitle = "Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011",

note = "11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 ; Conference date: 11-12-2011 Through 11-12-2011",

}

Dai, L, Chen, H & Li, X 2011, Improving sentiment classification using feature highlighting and feature bagging. in Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011., 6137361, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 61-66, 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011, Vancouver, BC, Canada, 11/12/11. https://doi.org/10.1109/ICDMW.2011.96

Improving sentiment classification using feature highlighting and feature bagging. / Dai, Lin; Chen, Hechun; Li, Xuemei.
Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011. 2011. p. 61-66 6137361 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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

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AU - Chen, Hechun

AU - Li, Xuemei

PY - 2011

Y1 - 2011

N2 - Sentiment classification is an important data mining task. Previous researches tried various machine learning techniques while didn't make fully use of the difference among features. This paper proposes a novel method for improving sentiment classification by fully exploring the different contribution of features. The method consists of two parts. First, we highlight sentimental features by increasing their weight. Second, we use bagging to construct multiple classifiers on different feature spaces and combine them into an aggregating classifier. Extensive experiments show that the method can evidently improve the performance of sentiment classification.

AB - Sentiment classification is an important data mining task. Previous researches tried various machine learning techniques while didn't make fully use of the difference among features. This paper proposes a novel method for improving sentiment classification by fully exploring the different contribution of features. The method consists of two parts. First, we highlight sentimental features by increasing their weight. Second, we use bagging to construct multiple classifiers on different feature spaces and combine them into an aggregating classifier. Extensive experiments show that the method can evidently improve the performance of sentiment classification.

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KW - Sentiment analysis

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M3 - Conference contribution

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Improving sentiment classification using feature highlighting and feature bagging

Abstract

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Conference

Keywords

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