The paper studies sentiment analysis by applying the twin objective function SVM
including NPSVM and TWSVM. From the experiments
we concluded that twin objective function SVM are prior to single objective function SVM in accuracy and stability.
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references
V. N. Vapnik, An overview of statistical learning theory[J]. IEEE Trans Neural Networks, 1999, 10(1): 988-999. DOI: 988
Tao Li, Ti Zhang, Vikas Sindhwani. A non-negative matrix tri-factorization approach to sentiment classification with lexical prior knowledge[C]//proceedings of the Joint Conference of the 47th Annual Ueeting of the ACL and the 4th International Joint Conference on Natural Language Proceessing of the AFNLP. stroudsburg, PA, USA: IEEE, 2009: 244-252.
Pang B, Lee L, Vaithyanath S. Thumbs up sentiment classification using machine learning technique[J]. Proc of the EMLP 2002, 10(7): 79-86. DOI: 79
Yingjie Tian, Xuchan Ju, Zhiquan Qi. Nonparallel support vector for pattern classification[J]. IEEE Trans Cybernetics, 2014, 44(7): 1067-1076. DOI: 1067
Ayadeva R K, Khemchandani R, Chandra S. Twin support vector machines for pattern classification[J]. IEEE Trans Pattern Anal Mach Intell, 2011, 29(5): 905-910. DOI: 905
Ahmed Abbasi, Hsinchun Chen, Arab Salem. Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums[J]. Journal ACM Transactions on Infarmation Systems Tois Homepage archive, 2008, 26(3): 1-12. DOI: 1