Sentiment analysis using Support Vector Machine

Sentiment analysis is treated as a classification task as it classifies the orientation of a text into either positive or negative. This paper describes experimental results that applied Support Vector Machine (SVM) on benchmark datasets to train a sentiment classifier. N-grams and different weighting scheme were used to extract the most classical features. It also explores Chi-Square weight features to select informative features for the classification. Experimental analysis reveals that by using Chi-Square feature selection may provide significant improvement on classification accuracy.

[1]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[2]  L. Ladha,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[3]  Ronen Feldman,et al.  Techniques and applications for sentiment analysis , 2013, CACM.

[4]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[5]  Yong Shi,et al.  The Role of Text Pre-processing in Sentiment Analysis , 2013, ITQM.

[6]  Luis Alfonso Ureña López,et al.  Técnicas de clasificación de opiniones aplicadas a un corpus en español , 2011, Proces. del Leng. Natural.

[7]  Jorge Bernardino,et al.  Comparison of data mining techniques and tools for data classification , 2013, C3S2E '13.

[8]  Serkan Günal,et al.  Subspace based feature selection for pattern recognition , 2008, Inf. Sci..

[9]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[10]  Songbo Tan,et al.  A survey on sentiment detection of reviews , 2009, Expert Syst. Appl..

[11]  Mohamed M. Mostafa,et al.  More than words: Social networks' text mining for consumer brand sentiments , 2013, Expert Syst. Appl..

[12]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[13]  Sri Ramakrishna,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[14]  Serkan Günal,et al.  A novel probabilistic feature selection method for text classification , 2012, Knowl. Based Syst..

[15]  Peter Willett,et al.  Readings in information retrieval , 1997 .

[16]  Luis Alfonso Ureña López,et al.  Experiments with SVM to classify opinions in different domains , 2011, Expert Syst. Appl..

[17]  Muhammad Abdul-Mageed,et al.  SAMAR: Subjectivity and sentiment analysis for Arabic social media , 2014, Comput. Speech Lang..

[18]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[19]  Maite Taboada,et al.  Methods for Creating Semantic Orientation Dictionaries , 2006, LREC.

[20]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[21]  Serkan Günal,et al.  The impact of preprocessing on text classification , 2014, Inf. Process. Manag..

[22]  Hsinchun Chen,et al.  Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums , 2008, TOIS.

[23]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[24]  Hassan Mathkour,et al.  Comparing text classifiers for sports news , 2012 .

[25]  J Allan,et al.  Readings in information retrieval. , 1998 .