Affect Analysis of Web Forums and Blogs Using Correlation Ensembles

Analysis of affective intensities in computer-mediated communication is important in order to allow a better understanding of online users' emotions and preferences. Despite considerable research on textual affect classification, it is unclear which features and techniques are most effective. In this study, we compared several feature representations for affect analysis, including learned n-grams and various automatically and manually crafted affect lexicons. We also proposed the support vector regression correlation ensemble (SVRCE) method for enhanced classification of affect intensities. SVRCE uses an ensemble of classifiers each trained using a feature subset tailored toward classifying a single affect class. The ensemble is combined with affect correlation information to enable better prediction of emotive intensities. Experiments were conducted on four test beds encompassing web forums, blogs, and online stories. The results revealed that learned n-grams were more effective than lexicon-based affect representations. The findings also indicated that SVRCE outperformed comparison techniques, including Pace regression, semantic orientation, and WordNet models. Ablation testing showed that the improved performance of SVRCE was attributable to its use of feature ensembles as well as affect correlation information. A brief case study was conducted to illustrate the utility of the features and techniques for affect analysis of large archives of online discourse.

[1]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[2]  Efstathios Stamatatos,et al.  Music Performer Recognition Using an Ensemble of Simple Classifiers , 2002, ECAI.

[3]  Pero Subasic,et al.  Affect analysis of text using fuzzy semantic typing , 2001, IEEE Trans. Fuzzy Syst..

[4]  Karrie Karahalios,et al.  Visualizing Conversation , 1999, J. Comput. Mediat. Commun..

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

[6]  Janyce Wiebe,et al.  Tracking Point of View in Narrative , 1994, Comput. Linguistics.

[7]  Chung-Hsien Wu,et al.  Emotion recognition from text using semantic labels and separable mixture models , 2006, TALIP.

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

[9]  Kong Joo Lee,et al.  Automatic Affect Recognition Using Natural Language Processing Techniques and Manually Built Affect Lexicon , 2006, IEICE Trans. Inf. Syst..

[10]  Gilad Mishne,et al.  Predicting Movie Sales from Blogger Sentiment , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[11]  D TurneyPeter,et al.  Measuring praise and criticism , 2003 .

[12]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[13]  J. Wade Davis,et al.  Statistical Pattern Recognition , 2003, Technometrics.

[14]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[15]  Jonathon Read,et al.  Recognising Affect in Text using Pointwise-Mutual Information , 2004 .

[16]  Ian H. Witten,et al.  Stacked generalization: when does it work? , 1997, IJCAI 1997.

[17]  Mitsuru Ishizuka,et al.  Emotion Estimation and Reasoning Based on Affective Textual Interaction , 2005, ACII.

[18]  Gilad Mishne,et al.  Capturing Global Mood Levels using Blog Posts , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[19]  Henry Lieberman,et al.  A model of textual affect sensing using real-world knowledge , 2003, IUI '03.

[20]  Hsinchun Chen,et al.  Textual Analysis of Stock Market Prediction Using Financial News Articles , 2006, AMCIS.

[21]  Carlo Strapparava,et al.  Developing Affective Lexical Resources , 2004, PsychNology J..

[22]  Shlomo Argamon,et al.  Stylistic text classification using functional lexical features , 2007, J. Assoc. Inf. Sci. Technol..

[23]  Rosalind W. Picard.  Affective Computing , 1997 .

[24]  Salvatore J. Stolfo,et al.  A Comparative Evaluation of Meta-Learning Strategies over Large and Distributed Data Sets , 2008 .

[25]  Ze-Jing Chuang,et al.  Multi-Modal Emotion Recognition from Speech and Text , 2004, ROCLING/IJCLCLP.

[26]  Shlomo Argamon,et al.  Choosing the Right Bigrams for Information Retrieval , 2004 .

[27]  Michael L. Littman,et al.  Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.

[28]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[29]  Ian Witten,et al.  Data Mining , 2000 .

[30]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[31]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

[33]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Gregory Grefenstette,et al.  Coupling Niche Browsers and Affect Analysis for an Opinion Mining Application , 2004, RIAO.

[35]  Kamal Nigam,et al.  Towards a Robust Metric of Opinion , 2004 .

[36]  Kevin J. Cherkauer.  Human Expert-level Performance on a Scientiic Image Analysis Task by a System Using Combined Artiicial Neural Networks , 1996 .

[37]  G. Mishne.  Experiments with Mood Classification in , 2005 .

[38]  Gregory Grefenstette,et al.  Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes , 2006, Computing Attitude and Affect in Text.

[39]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[40]  Marti A. Hearst.  Direction-based text interpretation as an information access refinement , 1992 .

[41]  Janyce Wiebe,et al.  Learning Subjective Language , 2004, CL.