Imbalanced sentiment classification

Shoushan Li; Guodong Zhou; Zhongqing Wang; Yat Mei Lee; Rangyang Wang

doi:10.1145/2063576.2063994

Imbalanced sentiment classification

Shoushan Li, Guodong Zhou, Zhongqing Wang, Yat Mei Lee, Rangyang Wang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

39 Citations (Scopus)

Abstract

Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.

Original language	English
Title of host publication	CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
Pages	2469-2472
Number of pages	4
DOIs	https://doi.org/10.1145/2063576.2063994
Publication status	Published - 13 Dec 2011
Externally published	Yes
Event	20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom Duration: 24 Oct 2011 → 28 Oct 2011

Conference

Conference	20th ACM Conference on Information and Knowledge Management, CIKM'11
Country/Territory	United Kingdom
City	Glasgow
Period	24/10/11 → 28/10/11

Keywords

imbalanced classification
opinion mining
sentiment classification

ASJC Scopus subject areas

General Decision Sciences
General Business,Management and Accounting

More information

10.1145/2063576.2063994

Cite this

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title = "Imbalanced sentiment classification",

abstract = "Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.",

keywords = "imbalanced classification, opinion mining, sentiment classification",

author = "Shoushan Li and Guodong Zhou and Zhongqing Wang and Lee, {Yat Mei} and Rangyang Wang",

year = "2011",

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day = "13",

doi = "10.1145/2063576.2063994",

language = "English",

isbn = "9781450307178",

pages = "2469--2472",

booktitle = "CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management",

note = "20th ACM Conference on Information and Knowledge Management, CIKM'11 ; Conference date: 24-10-2011 Through 28-10-2011",

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T1 - Imbalanced sentiment classification

AU - Li, Shoushan

AU - Zhou, Guodong

AU - Wang, Zhongqing

AU - Lee, Yat Mei

AU - Wang, Rangyang

PY - 2011/12/13

Y1 - 2011/12/13

N2 - Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.

AB - Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.

KW - imbalanced classification

KW - opinion mining

KW - sentiment classification

UR - http://www.scopus.com/inward/record.url?scp=83055165890&partnerID=8YFLogxK

U2 - 10.1145/2063576.2063994

DO - 10.1145/2063576.2063994

M3 - Conference article published in proceeding or book

SN - 9781450307178

SP - 2469

EP - 2472

BT - CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management

T2 - 20th ACM Conference on Information and Knowledge Management, CIKM'11

Y2 - 24 October 2011 through 28 October 2011

ER -

Imbalanced sentiment classification

Abstract

Conference

Keywords

ASJC Scopus subject areas

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