ReseaRch of lexical appRoach and machine leaRning methods foR sentiment analysis

The machine learning approach is represented with two methods: the maximum entropy method and support vector machine. Text representation for the maximum entropy method includes the information about the proportion of positive and negative words and collocations, the quantity of interrogation and exclamation marks, emoticons, obscene language. For the support vector machine binary vectors with cosine normalization are built on texts.

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

[2]  Natalia V. Loukachevitch,et al.  Extraction of Russian Sentiment Lexicon for Product Meta-Domain , 2012, COLING.

[3]  Н.В. Лукашевич,et al.  Sentiment analysis track at ROMIP 2011 , 2012 .

[4]  Jian Su,et al.  Supervised and Traditional Term Weighting Methods for Automatic Text Categorization , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[8]  Yulan He,et al.  Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis , 2012, TALIP.

[9]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

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

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

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Harith Alani,et al.  Alleviating Data Sparsity for Twitter Sentiment Analysis , 2012, #MSM.

[14]  Philip S. Yu,et al.  A holistic lexicon-based approach to opinion mining , 2008, WSDM '08.

[15]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.