poster

CONR: A Novel Method for Sentiment Word Identification

Authors:

Jiguang Liang,

Xiaofei Zhou,

Yue Hu,

Li Guo,

Shuo BaiAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 1943 - 1946

https://doi.org/10.1145/2661829.2662015

Published: 03 November 2014 Publication History

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Abstract

Sentiment word identification (SWI) is of high relevance to sentiment analysis technologies and applications. Currently most SWI methods heavily rely on sentiment seed words that have limited sentiment information. Even though there emerge non-seed approaches based on sentiment labels of documents, but in which the context information has not been fully considered. In this paper, based on matrix factorization with co-occurrence neighbor regularization which is derived from context, we propose a novel non-seed model called CONR for SWI. Instead of seed words, CONR exploits two important factors: sentiment matching and sentiment consistency for sentiment word identification. Experimental results on four publicly available datasets show that CONR can outperform the state of-the-art methods.

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Cited By

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Yuan SWu XXiang Y(2018)Task-specific word identification from short texts using a convolutional neural network1Intelligent Data Analysis10.3233/IDA-17341322:3(533-550)Online publication date: 7-May-2018
https://doi.org/10.3233/IDA-173413
Liang JZhang KZhou XHu YTan JBai S(2016)Leveraging Latent Sentiment Constraint in Probabilistic Matrix Factorization for Cross-domain Sentiment ClassificationProcedia Computer Science10.1016/j.procs.2016.05.35380:C(366-375)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.05.353
Li JRao YJin FChen HXiang X(2016)Multi-label maximum entropy model for social emotion classification over short textNeurocomputing10.1016/j.neucom.2016.03.088210:C(247-256)Online publication date: 19-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.03.088
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Index Terms

CONR: A Novel Method for Sentiment Word Identification
1. Applied computing

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Published In

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

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Published: 03 November 2014

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November 3 - 7, 2014

Shanghai, China

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CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Yuan SWu XXiang Y(2018)Task-specific word identification from short texts using a convolutional neural network1Intelligent Data Analysis10.3233/IDA-17341322:3(533-550)Online publication date: 7-May-2018
https://doi.org/10.3233/IDA-173413
Liang JZhang KZhou XHu YTan JBai S(2016)Leveraging Latent Sentiment Constraint in Probabilistic Matrix Factorization for Cross-domain Sentiment ClassificationProcedia Computer Science10.1016/j.procs.2016.05.35380:C(366-375)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.05.353
Li JRao YJin FChen HXiang X(2016)Multi-label maximum entropy model for social emotion classification over short textNeurocomputing10.1016/j.neucom.2016.03.088210:C(247-256)Online publication date: 19-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.03.088
Park KKim JPark JCha MNam JYoon SRhim EBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Mining the Minds of Customers from Online Chat LogsProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806621(1879-1882)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806621
Liang JZhou XGuo LBai SGangemi ALeonardi SPanconesi A(2015)Feature Selection for Sentiment Classification Using Matrix FactorizationProceedings of the 24th International Conference on World Wide Web10.1145/2740908.2742741(63-64)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2740908.2742741
Lin ZWang WJin XLiang JMeng DGangemi ALeonardi SPanconesi A(2015)A Word Vector and Matrix Factorization Based Method for Opinion Lexicon ExtractionProceedings of the 24th International Conference on World Wide Web10.1145/2740908.2742713(67-68)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2740908.2742713
Liang JZhou XHu YGuo LBai S(2015)Optimization-based model for determining words' sentiment orientations2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFCOMW.2015.7179356(87-88)Online publication date: Apr-2015
https://doi.org/10.1109/INFCOMW.2015.7179356
Liang JZhou XLiu PGuo L(2015)Latent sentiment representation for sentiment feature selection2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFCOMW.2015.7179348(71-72)Online publication date: Apr-2015
https://doi.org/10.1109/INFCOMW.2015.7179348
Liang JZhou XHu YGuo LBai S(2015)Sentiment Word Identification with Sentiment Contextual FactorsWeb Technologies and Applications10.1007/978-3-319-25255-1_18(215-226)Online publication date: 13-Nov-2015
https://doi.org/10.1007/978-3-319-25255-1_18

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Recommendations

Towards building a high-quality microblog-specific Chinese sentiment lexicon

Sentiment Groups as Features of a Classification Model Using a Spanish Sentiment Lexicon: A Hybrid Approach

A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

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