Enhancing Sentiment Classification Performance Using Bi-Tagged Phrases

Sentiment analysis research mainly aims to determine the orientation of an opinionated stretch of text into positive or negative polarity. The key motivation of sentiment analysis is getting to know what consumers think about products and services by analyzing their opinions on online portals, blogs, discussion boards, reviews etc. The main objective of this paper is to incorporate the information of POS-based sentiment-rich phrases in a machine-learning algorithm that determines the semantic orientation of a given text. In this paper, bi-tagged phrases are used as features in combination with unigram features for sentiment classification. Joint feature vectors of unigrams and bi-tagged phrases have high dimensions consisting of noisy and irrelevant features. Therefore, a feature selection method is used to select only relevant features from the feature vector. Experimental results show that the combination of prominent unigrams and bi-tagged phrases outperforms other features for sentiment classification in a movie review dataset.

[1]  Shouxun Lin,et al.  Dependency Forest for Sentiment Analysis , 2012 .

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

[3]  Erik Cambria,et al.  Big Social Data Analysis , 2013 .

[4]  Vincent Ng,et al.  Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews , 2006, ACL.

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

[6]  Vasileios Hatzivassiloglou,et al.  Predicting the Semantic Orientation of Adjectives , 1997, ACL.

[7]  Deyu Li,et al.  A Feature Selection Method Based on Fisher's Discriminant Ratio for Text Sentiment Classification , 2009, WISM.

[8]  Timothy O'Keefe.  Feature Selection and Weighting Methods in Sentiment Analysis , 2009 .

[9]  Haixun Wang,et al.  Isanette: A Common and Common Sense Knowledge Base for Opinion Mining , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[10]  Erik Cambria,et al.  Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining , 2013, BICA 2013.

[11]  Nirmalie Wiratunga,et al.  Selecting Bi-Tags for Sentiment Analysis of Text , 2007, SGAI Conf..

[12]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[13]  Jin Zhang,et al.  An empirical study of sentiment analysis for chinese documents , 2008, Expert Syst. Appl..

[14]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

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

[16]  Rui Xia,et al.  Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification , 2013, IEEE Intelligent Systems.

[17]  Deyu Li,et al.  A feature selection method based on improved fisher's discriminant ratio for text sentiment classification , 2011, Expert Syst. Appl..

[18]  Namita Mittal,et al.  Optimal Feature Selection for Sentiment Analysis , 2013, CICLing.

[19]  Rui Xia,et al.  Ensemble of feature sets and classification algorithms for sentiment classification , 2011, Inf. Sci..