Sentiment Computing for Visual Emotion Generation on Social Media using Text Mining
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : Nayana More, Deepali Jadhav|
|DOI : 10.14445/22312803/IJCTT-V66P101|
MLA Style: Nayana More, Deepali Jadhav "Sentiment Computing for Visual Emotion Generation on Social Media using Text Mining" International Journal of Computer Trends and Technology 66.1 (2018): 1-7.
APA Style:Nayana More, Deepali Jadhav (2018). Sentiment Computing for Visual Emotion Generation on Social Media using Text Mining. International Journal of Computer Trends and Technology, 66(1), 1-7.
The fast increase of the World Wide Web has helped increased online communication and opened up newer streets for the general public to post their opinions online. This has led to a generation of large amounts of online content rich in user opinions, sentiments, emotions, and evaluations. We need computational approaches to successfully analyse this online content, recognize and aggregate relevant information, and draw useful conclusions. Much of the current work in this direction has typically focused on recognizing the polarity of sentiment (positive/negative). In this writing, we have suggested a system that recognizes the emotion from the text of Social networking websites by using a modified approach that uses affective word based and sentence context level emotion classification method. Also to adequately express the emotion of a user we have developed a visual image generation approach that generates images according to emotion in text.
 C. O. Alm, D. Roth, and R. Sproat, "Emotions from the text: machine learning for text-based emotion prediction," in Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Vancouver, British Columbia, Canada: Association for Computational Linguistics, 2005, pp. 579–586.
 C. Strapparava and R. Mihalcea, “Learning to identify emotions in text,” in Proc. of the 23rd Annual ACM Symposium on Applied Computing, SAC’08, 2008, pp. 1556–1560.
 Shenghua Bao, Shengliang Xu, Li Zhang, Rong Yan, Zhong Su, Dingyi Han, and Yong Yu “Mining Social Emotions from Affective Text”.
 Cheng-Zhong Xu, Tamer I. Ibrahim, “A Keyword-Based Semantic Prefetching Approach in Internet News Services” IEEE conference on Knowledge and Data Engineering, vol. 16, no. 5, May 2004
 C. Yang, K. H.-Y. Lin, and H.-H. Chen, “Emotion classification using web blog corpora,” in Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence, WI’07, 2007, pp. 275– 278.
 A. J. Gill, D. Gergle, R. M. French, and J. Oberlander, “Emotion rating from short blog texts,” in Proc. of CHI’08 on Human factors in computing systems, 2008, pp. 1121–1124.
 Cheng-Yu Lu , William W.Y. Hsu, Hsing-Tsung Peng, Jen-Ming Chung “ Emotion Sensing for Internet Chatting: A WebMining Approach for Affective Categorization of Events” 13th IEEE International Conference on Computational Science and Engineering, 2009
 C. Strapparava and A. Valitutti, “Wordnet-affect: an affective extension of wordnet,” in Proc. of the 4th international conference on Language Resources and Evaluation, LREC’04, 2004
 Mohamed Yassine, Hazem Hajj, “A Framework for Emotion Mining from Text in Online Social Networks”, IEEE International Conference on Data Mining Workshops, 2010.
 R. Cai, C. Zhang, C. Wang, L. Zhang, and W.-Y.Ma., "Music sense: contextual music recommendation using emotional allocation," in Proc. of the 15th international conference on Multimedia, 2007, pp. 553–556.
 Amitava Das, Sivaji Bandyopadhyay, “Theme Detection an Exploration of Opinion Subjectivity”, IEEE International Conference on Data Mining Workshops, 2009.
 DR. Yashpal Singh, Alok Singh Chauhan, “neural networks in data mining” Journal of Theoretical and Applied Information Technology,vol.5, pp.37- 42, 2005 – 2009
 Lina L. Dhande1 and Dr. Prof. Girish K. Patnaik2, “Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier”,International Journal of Emerging Trends & Technology in Computer Science (IJCTTCS). Volume 3, Issue 4 July-August 2014 ISSN 2278-6856.
 Mukesh C. Jain, V. Y. Kulkarni, “TexEmo: Conveying Emotion from Text- The Study”, International Journal of Computer Applications (0975 – 8887) pp.43-50, Vol 86 – No 4, January 2014.
 Wu, Chung-Hsien & Chuang, Ze-Jing & Lin, Yu-Chung. (2006). Emotion recognition from text using semantic labels and separable mixture models. ACM Trans. Asian Lang. Inf. Process.. 5. 165-183. 10.1145/1165255.1165259.
 Ma, Chunling & Prendinger, Helmut & Ishizuka, Mitsuru. (2005). Emotion Estimation and Reasoning Based on Affective Textual Interaction. 622-628. 10.1007/11573548_80.
 Yassine, Mohamed & Hajj, Hazem. (2010). A Framework for Emotion Mining from Text in Online Social Networks. Proceedings - IEEE International Conference on Data Mining, ICDM. 1136-1142. 10.1109/ICDMW.2010.75.
 Saima Aman1 and Stan Szpakowicz “Identifying Expressions of Emotion in Text”, School of Information Technology and Engineering, University of Ottawa, Ottawa,
 Rutuja Karkar1, Shweta Nagdev, Pranjal Gangrade, Deepali D. Gatade"Transformation of Sentimental Impact for Documents",International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072.
Emotion, Sentiment, Classification, Social Networking, Recognizing, Accuracy, Extraction, Tagging, Detection, Pre-processing;