Feeler: Emotion classification of text using vector space model

AISB 2008 Convention: Communication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Affective Language in Human and Machine

Volumn 1, Issue , 2008, Pages 53-59

  Danisman, Taner ^a   Alpkocak, Adil ^a  

^a DOKUZ EYLUL UNIVERSITY   (Turkey)

Author keywords

Emotion detection in text; Emotion perception; Human computer interaction; Vector space model

Indexed keywords

AUTOMATIC CLASSIFICATION; CLASSIFICATION OF EMOTIONS; CROSS-VALIDATION TECHNIQUE; DATA SETS; EMOTION CLASSIFICATION; EMOTION DETECTION; EMOTION DETECTION IN TEXT; F-MEASURE; HUMAN EMOTION; INTERNATIONAL SURVEY; KAPPA VALUES; NAIVE BAYES; NAVIE BAYES; SENTIMENT ANALYSIS; SVM-BASED CLASSIFIERS; TEXT CLASSIFICATION; VECTOR SPACE MODEL; VECTOR SPACE MODELS; VIDEO PLAYERS;

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; EXPERIMENTS; SUPPORT VECTOR MACHINES; TEXT PROCESSING; VECTOR SPACES;

BEHAVIORAL RESEARCH;

EID: 84859256606     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. G. Mishne, Experiments with mood classification in blog posts. In: Style2005 - 1st Workshop on Stylistic Analysis of Text for Information Access, at SIGIR (2005).
2. K. R. Scherer and H.G. Wallbott, Evidence for universality and cultural variation of differential emotion response patterning. Journal of Personality and Social Psychology, 66:310-328 (1994). 2 c Finding Nemo is a Copyright owned by Walt Disney and Pixar Entertainment.
3. D. A. Medler, A. Arnoldussen, J.R. Binder, and M.S. Seidenberg, The Wisconsin Perceptual Attribute Ratings Database. http://www.neuro.mcw.edu/ ratings/(2005).
4. C. Strapparava and A. Valitutti., WordNet-Affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), Lisbon, pp. 1083-1086, May (2004).
5. A. C. Boucouvalas and X. Zhe, Text-to-Emotion. Engine for Real Time Internet Communication. In: Proceedings of the 3rd International Symposium on CSNDSP, Staffordshire University, UK, pp. 164-168, (2002).
6. A. Valitutti, C. Strapparava, and O. Stock, Developing affective lexical resources. PsychNology Journal, 2(1):61-83 (2004).
7. M. Shaikh, H. Prendinger, and M. Ishizuka, Emotion sensitive news agent: An approach towards user centric emotion sensing from the news. The 2007 IEEE/WIC/ACM International Conference on Web Intelligence (WI-07), Silicon Valley, USA, pp. 614-620 Nov. (2007).
8. M. Shaikh, H. Prendinger, and M. Ishizuka, SenseNet: A linguistic tool to visualize numerical-valence based sentiment of textual data (Poster). Proceedings 5th International Conference on Natural Language Processing (ICON-07), Hyderabad, India, pp. 147-152, (2007).
9. M. Shaikh, H. Prendinger, and M. Ishizuka, A cognitively based approach to affect sensing from text, In: Proceedings of 10th Int'l Conf. on Intelligent User Interface (IUI 2006), Sydney, Australia, pp.349-351 (2006).
10. C. O. Alm, D. Roth, and R. Sproat, Emotions from text: Machine learning for text-based emotion prediction. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pages 579-586, Vancouver, British Columbia, Canada, (2005).
11. H. Liu, H. Lieberman, and T. Selker, A model of textual affect sensing using real-world Knowledge. In: Proceedings of International Conference on Intelligent User Interfaces (IUI-03) pp. 125-132, (2003).
12. F. Sugimoto and M. Yoneyama, A Method for Classifying Emotion of Text based on Emotional Dictionaries for Emotional Reading, In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, Austria, pp. 91-96 (2006).
13. Z.J. Chuang and H. Wu, Multi-Modal Emotion Recognition from Speech and Text. International Journal of Computational Linguistics and Chinese Language Processing, 9(2):1-18 (2004).
14. J. W. Pennebaker, M. E. Francis, and R. J. Booth, Linguistic Inquiry and Word Count: LIWC 2001. Mahwah, NJ: Lawrence Erlbaum, (2001).
15. H. Liu and P. Singh, ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal 22(4):211-226. Kluwer Academic Publishers, (2004).
16. I. H. Witten and E. Frank (2005) Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, (2005).
17. E. Boiy, P. Hens, K. Deschacht, and M.-F. Moens, (2007) Automatic Sentiment Analysis in On-line Text, ELPUB2007. Openness in Digital Publishing: Awareness, Discovery and Access In : Proceedings of the 11th International Conference on Electronic Publishing, Vienna, Austria 13-15, June (2007).
18. D. Zeimpekis and E. Gallopoulos, Tmg: A matlab toolbox for generating term-document matrices from text collections. Technical report, University of Patras, Greece, (2005).
19. C. Strappava and R. Mihalcea, SemEval-2007 Task 14: Affective Text, In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 70-74, Prague, June (2007).