Summary of the paper

Title Automatic Annotation of Word Emotion in Sentences Based on Ren-CECps
Authors Changqin Quan and Fuji Ren
Abstract Textual information is an important communication medium contained rich expression of emotion, and emotion recognition on text has wide applications. Word emotion analysis is fundamental in the problem of textual emotion recognition. Through an analysis of the characteristics of word emotion expression, we use word emotion vector to describe the combined basic emotions in a word, which can be used to distinguish direct and indirect emotion words, express emotion ambiguity in words, and express multiple emotions in words. Based on Ren-CECps (a Chinese emotion corpus), we do an experiment to explore the role of emotion word for sentence emotion recognition and we find that the emotions of a simple sentence (sentence without negative words, conjunctions, or question mark) can be approximated by an addition of the word emotions. Then MaxEnt modeling is used to find which context features are effective for recognizing word emotion in sentences. The features of word, N-words, POS, Pre-N-words emotion, Pre-is-degree-word, Pre-is-negativeword, Pre-is-conjunction and their combination have been experimented. After that, we use the two metrics: Kappa coefficient of agreement and Voting agreement to measure the word annotation agreement of Ren-CECps. The experiments on above context features showed promising results compared with word emotion agreement on people's judgments.
Topics Emotion Recognition/Generation
Full paper Automatic Annotation of Word Emotion in Sentences Based on Ren-CECps
Slides -
Bibtex @InProceedings{QUAN10.662,
  author = {Changqin Quan and Fuji Ren},
  title = {Automatic Annotation of Word Emotion in Sentences Based on Ren-CECps},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
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