Proceedings of the 20th ACM International Conference on Information and Knowledge Management 2011
DOI: 10.1145/2063576.2063994
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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 clusteringbased stratified under-sampling framework and a centroiddirected smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different… Show more

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Cited by 55 publications
(48 citation statements)
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References 15 publications
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“…The final classifier is built using the combined features. Li and Zong (2008) built a meta-classifier (called CLF) using the outputs of each base classifier constructed in each domain. Other works along similar lines include (Andreevskaia and Bergler, 2008, Bollegala et al, 2011, He et al, 2011, Ku et al, 2009, Li et al, 2012, Pan and Yang, 2010, Tan et al, 2007, Wu et al, 2009, Xia and Zong, 2011, Yoshida et al, 2011.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…The final classifier is built using the combined features. Li and Zong (2008) built a meta-classifier (called CLF) using the outputs of each base classifier constructed in each domain. Other works along similar lines include (Andreevskaia and Bergler, 2008, Bollegala et al, 2011, He et al, 2011, Ku et al, 2009, Li et al, 2012, Pan and Yang, 2010, Tan et al, 2007, Wu et al, 2009, Xia and Zong, 2011, Yoshida et al, 2011.…”
Section: Related Workmentioning
confidence: 99%
“…We compare our proposed LSC model with Naïve Bayes (NB), SVM 1 , and CLF (Li and Zong, 2008). Note that NB and SVM can only work on a single domain data.…”
Section: Balanced Class Distributionmentioning
confidence: 99%
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“…Then a summary is produced by opting out sentences that carries distinctive feature information [21].…”
Section: (3) Opinion Summarizationmentioning
confidence: 99%
“…In fact, a nonuniform classes' distribution can lead to a partial learning, i.e., a model trained on an unbalanced dataset can tend to ignore the minority class, predicting samples as belonging to the majority one [4]. Therefore, in non-balanced class datasets, alternative solutions have been incorporated in both SL and SSL algorithms, either at data level, such as under and over sampling, or algorithm level, like costsensitive, active learning or even ensemble methods [2][3][4][5][6][7][8].…”
Section: Introductionmentioning
confidence: 99%