Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30101
A Proposed Approach for Emotion Lexicon Enrichment

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

Abstract:

Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: Document analysis, sentimental analysis, emotion detection, WEKA tool, NRC Lexicon.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126595

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 630

References:


[1] Bing Liu, Sentiment Analysis and Opinion Mining. Chicago, USA: Morgan & Claypool Publishers, 2012.
[2] P. Ekman, Universals and Cultural Differences in Facial Expressions of Emotions. Lincioln: University of Nebraska Press, 1972.
[3] ConceptNet5, (2010). Last retrieved 29 Novemeber 2015 (Online). http://conceptnet5.media.mit.edu/
[4] R. Ezhilarasi and R. Minu, "Automatic Emotion Recognition and Classification," in International Conference on Modelling Optimization and Computing, 2012.
[5] George A. Miller, "WordNet: a lexical database for English," Communications of the ACM, vol. 38, no. 11, pp. 39-41, 1995.
[6] M. Naveen Kumar and R. Suresh, "Emotion Detection using Lexical Chains," International Journal of Computer Applications, vol. 57, no. 4, 2012.
[7] Peter D. Turney and Michael L. Littman, "Measuring Praise and Criticism: Inference of Semantic Orientation from Association," ACM Transactions on Information Systems, vol. 21, pp. 315–346, 2003.
[8] J. Lei, Y. Raob, Q. Li, X. Quan, and Liu Wenyin, "Towards building a social emotion detection system for online news," Journal of Future Generation Computer Systems, vol. 37, pp. 438-448, 2013.
[9] 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). http://www.ijera.com/papers/Vol3_issue3/S33100107.pdf
[10] R. Tokuhisa, K. Inui, and Y. Matsumoto, "Emotion Classification Using Massive Examples Extracted from the Web," in the 22nd International Conference on Computational Linguistics, 2008.
[11] D. Inkpen, F. keshtkar, and D. Ghazi, "Analysis and Generation of Emotion in Texts," International Conference on Knowledge Engineering Principles and Techniques, 2009.
[12] T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms, 1st ed. Boston: Kluwer Academic Publishers, 2002.
[13] 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.
[14] S. Boa et al., "Mining Social Emotions from Affective Text," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 9, pp. 1658-1669, 2012.
[15] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze, Introduction to Information Retrieval. New York: Cambridge University, 2008.
[16] Emotion Research. (1990s) (Online). http://emotionresearch.net/toolbox/toolboxdatabase.
[17] C. Strapparava and R. Mihalcea, "SemEval-2007 Task 14: Affective Text," in The 4th International Workshop on Semantic Evaluations, 2007.
[18] 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.
[19] Mechanical Turk (2007) Retrieved from: https://www.mturk.com/mturk/welcome
[20] Carlo Strapparava and Alessandro Valitutti, "WordNet-Affect: An Affective Extension of WordNet,", Lisbon, 2004, pp. 1083-1086.
[21] Peter Cohen. (2005) https://www.mturk.com/mturk. (Online). https://www.mturk.com/mturk
[22] Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten Mark Hall. (2009) the University of Waikato. (Online). http://www.cs.waikato.ac.nz/ml/weka/
[23] 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.
[24] B. S., Landau, S., Leese, M. and Stahl, D. Everitt, Miscellaneous Clustering Methods, in Cluster Analysis, 5th ed. Chichester, UK: John Wiley & Sons, 2011.
[25] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second edition ed. Berkeley: Prentice Hall, 2003.
[26] G. Cleary and Leonard, E. Trigg John, "K*: An Instance- based Learner Using an Entropic Distance Measure,", 1995.
[27] Amit, Anshuman Sahu, Daniel Apley, and George Runger Shinde, "Preimages for Variation Patterns from Kernel PCA and Bagging," IIE Transactions, vol. 46, no. 5, 2014.
[28] Niels Landwehr and Mark Hall and Eibe Frank, "Logistic Model Trees," Machine Learning, vol. 95, no. 1, pp. 161-205, 2005.
[29] data-mining-business-intelligence. Last retrieved: 29 November 2015 (Online). http://data-mining.business-intelligence.uoc.edu/home/j48-decision-tree
[30] Marie-Catherine de Marneffe, Bill MacCartney, and Christopher D. Manning, "Generating Typed Dependency Parses from Phrase Structure Parses," in The fifth international conference on Language Resources and Evaluation, Genoa, Italy, 2006, pp. 449-454.
[31] Saif Mohammad, "Portable Features for Classifying Emotional Text," in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, North American, 2012, pp. 587-591. (Online). http://www.aclweb.org/anthology/N12-1071