Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30761
Documents Emotions Classification Model Based on TF-IDF Weighting Measure

Authors: Amira M. Idrees, Amr Mansour Mohsen, Hesham Ahmed Hassan


Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: Classification Algorithms, Emotion Detection, Weka tool, TF-IDF

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1049


[1] Bing Liu, Sentiment Analysis and Opinion Mining. Chicago, USA: Morgan & Claypool Publishers, 2012.
[2] Manish Sharma and Rahul Patel, "A Survey on Information Retrieval Models, Techniques and Applications," International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 11, pp. 542-545, 2013. (Online).
[3] Amira F. El Gohary, Torky I. Sultan, Maha A. Hana, and Mohamed M. El Dosoky, "A Computational Approach for Analyzing and Detecting Emotions in Arabic Text," International Journal of Engineering Research and Applications (IJERA), vol. 3, no. 3, pp. 100-107, May-Jun 2013. (Online).
[4] Xuren Wang and Qiuhui Zheng, "Text Emotion Classification Research Based on Improved Latent Semantic Analysis Algorithm," in Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), 2013.
[5] Barbara Martinazzo, Mariza Miola Dosciatti, and Emerson Cabrera Paraiso, "Identifying Emotions in Short Texts for Brazilian Brazilian Portuguese," in Brazilian conference on intelligent systems, Redes Neurais, 2012.
[6] Christian S. Perone, “Machine Learning: Cosine Similarity for Vector Space Models”, (2013) pyevolve.sourceforge. (Online).
[7] Dan Jurafsky and Christopher Manning. Coursera. (Online).
[8] D. Inkpen, F. keshtkar, and D. Ghazi, "Analysis and Generation of Emotion In Texts," International Conference on Knowledge Engineering Principles and Techniques, 2009.
[9] Chaitali G. Patil and Sandip S. Patil, "Use of Porter Stemming Algorithm and SVM for Emotion Extraction from News Headlines," International Journal of Electronics, Communication & Soft Computing Science and Engineering, vol. 2, no. 7, pp. 9-13, 2013.
[10] Landauer T. K. and S. T. Dumais, "A solution to Plato's problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge," Psychological Review, vol. 104, no. 1, pp. 211-240, 1997.
[11] Rish, Irina. (2001). "An empirical study of the naive Bayes classifier". IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence. (available online: PDF ( papers/RC22230.pdf), PostScript ( /people/r/rish/papers/
[12] Cheng-Yu Lu, Jen-Shin Hong, and Samuel Cruz-Lara, "Emotion Detection in Textual Information by Semantic Role Labeling and Web Mining Techniques," in the Third Taiwanese-French Conference on Information Technology, Nancy/France, 2006.
[13] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze, Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008.
[14] Saif Mohammad and Peter Turney, "Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon," in the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, California, 2010.
[15] Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani, "SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining," in Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC’10), Valletta, 2010, pp. 2200–2204.
[16] Mikula, G., Scherer, K. R., & Athenstaedt, U. (1998). The role of injustice in the elicitation of differential emotional reactions. Personality and Social Psychology Bulletin, 24(7), 769-783
[17] Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten Mark Hall. (2009) the University of Waikato. (Online).
[18] J. Platt, "Fast Training of Support Vector Machines using Sequential Minimal Optimization," in Advances in Kernel Methods - Support Vector Learning, B. Schoelkopf and C. Burges and A. Smola, Ed. UN: MIT Press, 1998, pp. 1-.
[19] B. S., Landau, S., Leese, M. and Stahl, D. Everitt, Miscellaneous Clustering Methods, in Cluster Analysis, 5th ed. Chichester, UK: John Wiley & Sons, 2011.
[20] G. Cleary and Leonard, E. Trigg John, "K*: An Instance- based Learner Using an Entropic Distance Measure,", 1995.
[21] Amit, Anshuman Sahu, Daniel Apley, and George Runger Shinde, "Preimages for Variation Patterns from Kernel PCA and Bagging," IIE Transactions, vol. 46, no. 5, pp. 1-, 2014.
[22] Niels Landwehr and Mark Hall and Eibe Frank, "Logistic Model Trees," Machine Learning, vol. 95, no. 1, pp. 161-205, 2005.
[23] data-mining business-intelligence. (Online).
[24] T., & Alpkocak, A. Danisman, "Feeler: Emotion classification of text using vector space model," AISB 2008 Convention Communication, Interaction and Social Intelligence, vol. 1, no. 1, p. p. 53, 2008.