Using an Emotion-based Model and Sentiment Analysis Techniques to Classify Polarity for Reputation

Online Reputation Management is a novel and active area in Computational Linguistics. Closely related to opinion mining and sentiment analysis, it incorporates new features to traditional tasks like polarity detection. In this paper, we study the feasibility of applying complex sentiment analysis methods to classifying polarity for reputation. We adapt an existing emotional concept-based system for sentiment analysis to determine polarity of tweets with reputational information about companies. The original system has been extended to work with texts in English and in Spanish, and to include a module for filtering tweets according to their relevance to each company. The resulting UNED system for profiling task participated in the first RepLab campaign. The experimental results prove that sentiment analysis techniques are a good starting point for creating systems for automatic detection of polarity for reputation.

[1]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[2]  Pablo Gervás,et al.  SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis , 2012, LREC.

[3]  Ted Pedersen,et al.  SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation , 2005, ACL.

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

[5]  Julio Gonzalo,et al.  Overview of RepLab 2012: Evaluating Online Reputation Management Systems , 2012, CLEF.

[6]  Andrea Esuli,et al.  Determining Term Subjectivity and Term Orientation for Opinion Mining , 2006, EACL.

[7]  Fabio Roli,et al.  An approach to the automatic design of multiple classifier systems , 2001, Pattern Recognit. Lett..

[8]  M. T. Lino,et al.  Proceedings of the 4th International Conference on Language Resources and Evaluation , 2004 .

[9]  German Rigau,et al.  Multilingual Central Repository version 3 . 0 : upgrading a very large lexical knowledge base , 2011 .

[10]  Hamish Cunningham,et al.  GATE-a General Architecture for Text Engineering , 1996, COLING.

[11]  Janyce Wiebe,et al.  Development and Use of a Gold-Standard Data Set for Subjectivity Classifications , 1999, ACL.

[12]  Eneko Agirre,et al.  Personalizing PageRank for Word Sense Disambiguation , 2009, EACL.

[13]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

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

[15]  Xavier Carreras,et al.  FreeLing: An Open-Source Suite of Language Analyzers , 2004, LREC.

[16]  Robert P. W. Duin,et al.  Is independence good for combining classifiers? , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[17]  Dietrich Klakow,et al.  Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification , 2010 .

[18]  Roi Blanco,et al.  FBM-Yahoo! at RepLab 2012 , 2012, CLEF.

[19]  Julio Gonzalo,et al.  UNED: Improving Text Similarity Measures without Human Assessments , 2012, SemEval@NAACL-HLT.

[20]  Julian Brooke,et al.  A SEMANTIC APPROACH TO AUTOMATED TEXT SENTIMENT ANALYSIS , 2009 .

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

[22]  Janyce Wiebe,et al.  Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis , 2009, CL.

[23]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[24]  Pablo Gervás,et al.  A Hybrid Approach to Emotional Sentence Polarity and Intensity Classification , 2010, CoNLL.

[25]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.