Recognizing sentence emotions based on polynomial kernel method using Ren-CECps

Emotion recogonition on text has wide applications. In this study we propose a method of emotion recognition at sentence level based on a relative large emotion annotation corpus (Ren-CECps). From this corpus, we get the emotion lexicons for the eight basic emotions (expect, joy, love, surprise, anxiety, sorrow, angry and hate). Statistics show that the emotion lexicons derived from Ren-CECps are used more often in real use of language for emotional expressions than HOWNET sentimental lexicons. Kernel methods are state-of-the-art for solving machine learning problems. Polynomial kernel (PK) method is used to compute the similarities between sentences and the eight emotion lexicons. Then the experiential knowledge derived from Ren-CECps is used to recognize whether the eight emotion categories are present in a sentence. This method obtain 62.7% F-measure.

[1]  Changqin Quan,et al.  Construction of a Blog Emotion Corpus for Chinese Emotional Expression Analysis , 2009, EMNLP.

[2]  Hsin-Hsi Chen,et al.  What emotions do news articles trigger in their readers? , 2007, SIGIR.

[3]  Marshall S. Smith,et al.  The general inquirer: A computer approach to content analysis. , 1967 .

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

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

[6]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[7]  Fuji Ren,et al.  Affective Information Processing and Recognizing Human Emotion , 2006, MFCSIT.

[8]  Sung-Hyon Myaeng,et al.  Determining Mood for a Blog by Combining Multiple Sources of Evidence , 2007 .

[9]  Andrew Ortony,et al.  The Referential Structure of the Affective Lexicon , 1987, Cogn. Sci..

[10]  Mitsuru Ishizuka,et al.  Textual Affect Sensing for Sociable and Expressive Online Communication , 2007, ACII.

[11]  Carlo Strapparava,et al.  The Affective Weight of Lexicon , 2006, LREC.

[12]  Stan Szpakowicz,et al.  Identifying Expressions of Emotion in Text , 2007, TSD.

[13]  Ling Lin,et al.  Leveraging Webpage Classification for Data Object Recognition , 2007 .

[14]  M. de Rijke,et al.  UvA-DARE ( Digital Academic Repository ) Using WordNet to measure semantic orientations of adjectives , 2004 .

[15]  Gerhard Weikum,et al.  Stylistic Analysis Of Text For Information Access , 2005 .

[16]  Janyce Wiebe,et al.  Effects of Adjective Orientation and Gradability on Sentence Subjectivity , 2000, COLING.

[17]  Nigel Collier,et al.  Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.

[18]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[19]  Shrikanth S. Narayanan,et al.  Toward detecting emotions in spoken dialogs , 2005, IEEE Transactions on Speech and Audio Processing.

[20]  Hsin-Hsi Chen,et al.  Tagging Heterogeneous Evaluation Corpora for Opinionated Tasks , 2006, LREC.

[21]  Philip J. Stone,et al.  Extracting Information. (Book Reviews: The General Inquirer. A Computer Approach to Content Analysis) , 1967 .

[22]  Cecilia Ovesdotter Alm,et al.  Emotions from Text: Machine Learning for Text-based Emotion Prediction , 2005, HLT.