Implementation of Emotion Generation and Summarization form Affective Text
Miss. Sayalee Sandeep Raut, Vidyalankar Institute of Technology; Prof. Kavita Pankaj Shirsat ,Vidyalankar Institute of Technology
Affective Text Mining, Emotional-Topic Model
Emotion Generation and Summarization form Affective Text deals with new aspect for categorizing the document based on the emotions such as Empathy, Touched, Boredom, Warmness, Amusement and Surprise. In order to predict the emotion contained in content a proposed model i.e. Emotion Topic Model is used. Using this it first generates a latent topic from emotions, followed by generating affective terms from each topic. First it separates emotion and word document and derived probabilities for it. The model which we proposed will utilize the complementary advantages of both emotion-term model and topic model. Emotion-topic model allows associating the terms i.e. words and emotions via topics which is more flexible. For classification we have used Naive Bayesian algorithm and Iteration based Nearest Neighbor Algorithm which will predict emotion accurately. For each emotion, we will be displaying emoticon and songs recommendation will be available for user. So that in future user can upload and enjoy their own choice of song based on their emotion which is detected from text.
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[1] C.Yang, K.H.-Y. Lin, and H.-H. Chen, “Emotion Classification Using Web Blog Corpora,” Proc. IEEE/WIC/ACM Int’l Conf. Web Intelligence (WI ’07), pp. 275-278, 2007.
[2] C. Strapparava and R. Mihalcea, “Learning to Identify Emotions in Text,” Proc. 23rd Ann. ACM Symp. Applied Computing (SAC ’08), pp. 1556-1560, 2008
[3] D.M.Blei, A.Y.Ng,and M.I.Jorden, ”Latent Dirichlet Allocation,”j.Machine Learning Research,Vol.3,pp.993-1022,2003
[4] Shenghua Bao,Shengliang Xu,Li Zhang, Rong Yan,Zhong Su, Dingyi Han, and Yong Yu “Mining Social Emotions from Affective Text” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 9, SEPTEMBER 2012
[5] Miss.Sayalee S. Raut, Prof.Kavita P. Shirsat, “Automatic Emotion Generation and Summarization form Perceptual Text – A Survey” International Journal of Science and Research (IJSR), Volume 4 Issue 10, October 2015
[6] A. Esuli and F. Sebastiani, “Sentiwordnet: A Pub-Licly Available Lexical Resource for Opinion Mining,” Proc. Fifth Int’l Conf. Language Resources and Evaluation (LREC ’06), 2006.
[7] T. Griffiths and M. Steyvers, “Finding Scientific Topics,” Proc. Nat’l Academy of Sciences USA, vol. 101, pp. 5228-5235, 2004.
[8] C. Strapparava and R. Mihalcea, “Learning to Identify Emotions in Text,” Proc. 23rd Ann. ACM Symp. Applied Computing (SAC ’08), pp. 1556-1560, and 2008.
[9] Tejasvini Patil and Sachin Patil, “Automatic Generation of Emotions for Social Networking using Text Mining” IEEE 2013
[10] Mohammed Yassine and Hazem Hajj, “A Framework for Mining from Text in online Social Networks” IEEE 2010
[11] D.M.Blei,A.Y.Ng,and M.I.Jorden, ”Latent Dirichlet Allocation,” j.Machine Learning Research,Vol.3,pp.993-1022,2003
[12] Carlo Strapparava and Alessandro Valitutti,“WordNet Affect: an Affective Extension of WordNet.”, LREC, 2004 - hnk.ffzg.hr
[13] M Hu, B Liu,” Mining and summarizing customer reviews”, Proceedings of the tenth ACM SIGKDD international 2004
[14] Vairaprakash Gurusamy and Subbu Kannan, “Pre-processing Techniques for Text Mining”, Conference Paper • October 2014
[15] http://gerardnico.com/wiki/data_mining/classification
Paper ID: GRDJEV01I080048
Published in: Volume : 1, Issue : 8
Publication Date: 2016-08-01
Page(s): 58 - 63
Published in: Volume : 1, Issue : 8
Publication Date: 2016-08-01
Page(s): 58 - 63