The week's pick
Reseach Article
Semantically Data Classification Analysis Algorithm for Social Media
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 154 - Number 11 |
| Year of Publication: 2016 |
| Authors: Ashwini Pal, Prakash Mishra |
10.5120/ijca2016911927
|
Ashwini Pal, Prakash Mishra . Semantically Data Classification Analysis Algorithm for Social Media. International Journal of Computer Applications. 154, 11 ( Nov 2016), 21-25. DOI=10.5120/ijca2016911927
Abstract
Sentiment Categorization adverts to the method approaches for classifying whether or not or no longer or not the feelings of textual content material are positive or terrible. Statistical methods supported term Presence and time period Frequency, mistreatment support Vector computing gadget are probably utilized for Sentiment Categorization. . Our technique is dependent on time interval weight programs that are used for information recuperation and sentiment categorization. It differs radically from these original methods on account that that of our mannequin of logarithmic differential time period frequency and declaration presence institution for sentiment classification. Terms with almost equal distribution in no doubt tagged documents and negatively labeled documents were categorized as a discontinue-phrase and discarded. The proportional distribution of a time interval to be categorised as stop-phrase used to be determined through an scan. We evaluated the proposed mannequin by means of evaluation it with state of art systems for sentiment classification.
References
- Hanjun Lee Business School, Korea “The Influence Of Negative Emotions In An Online Brand Community On Customer Innovation Activities ” 2014 47th Hawaii International Conference on System Science -978-1-4799-2504-9/14 $31.00 © 2014 IEEE
- Xia Hu, Lei Tang, Jiliang Tang, Huan Liu, “Exploiting Social Relations for Sentiment Analysisin Microblogging”, permission and/or a fee.WSDM ’13, February 4–8, 2013, Rome, Italy.Copyright 2013 ACM 978-1-4503-1869-3/13/02
- Fei Jiang, Anqi Cui, Yiqun Liu, Min Zhang, and Shaoping Ma, “Every Term Has Sentiment:Learning from Emoticon Evidencesfor Chinese Microblog Sentiment Analysis”,c Springer-Verlag Berlin Heidelberg 2013
- Eric Baucom,AzadeSanjari, Xiaozhong Liu,Miao Chen, “Mirroring the Real World in Social Media: Twitter,Geolocation, and Sentiment Analysis”,Copyright 2013 ACM 978-1-4503-2415-1/13/10
- Min Wang,Donglin Cao, Lingxiao Li,Shaozi Li, RongrongJi, “Microblog Sentiment Analysis Based on Cross-mediaBag-of-words Model”,ICIMCS’14, July 10–12, 2014, Xiamen, Fujian, China.Copyright 2014 ACM 978-1-4503-2810-4/14/07
- Felipe Bravo-Marquez, Marcelo Mendoza,Barbara Poblete, “Combining Strengths, Emotions and Polarities forBoosting Twitter Sentiment Analysis”,WISDOM’13, August 11 2013, Chicago, IL, USACopyright 2013 ACM 978-1-4503-2332-1/13/08
- Pedro Calais Guerra, Wagner Meira Jr.,Claire Cardie, “Sentiment Analysis on Evolving Social Streams:How Self-Report Imbalances Can Help”,WSDM’14, February 24–28, 2014, New York, New York, USA.Copyright 2014 ACM 978-1-4503-2351-2/14/02
- E. Cambria, B. Schuller, Y. Xia, and C. Havasi, “New Avenues in Opinion Mining and Sentiment Analysis,” Intelligent Systems, IEEE , vol.28, no.2, pp. 15-21, 2013.
- S. Baccianella, A. Esuli, and F. Sebastiani, ” Senti WordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining,” In Proc. of 7th Int’l Conf. on Language Resources and Evaluation, pp 2200-2204, 2010.
- A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "SentiFul: A Lexicon for Sentiment Analysis," In IEEE Trans. On Affective Computing, vol. 02, issue no. 01, pp. 22-36, 2011.
- Bollegala, D. Weir, and J. Carroll, "Crossm Domain Sentiment Classification using a Sentiment Sensitive Thesaurus,"Knowledge and Data Engineering, IEEE Transactions, vol.25, issue no.08, pp. 1, 0
- R. Xia and C. Zong, “A POS-based Ensemble Model for Cross-domain Sentiment Classification,” In Proc. of 5th Int’l Joint Conf. on Natural Language Processing, pp. 614–622, 2011.
- K. Ghag and K. Shah, "Comparative analysis of the techniques for Sentiment Analysis," In Proc. of Int’l Conf. on Advances in Technology and Engineering, pp. 1-7, 2013.
- G. Paltoglou and M. Thelwall, “A study of Information Retrieval weighting schemes for sentiment analysis,” In Proc. of 48th Annual Meeting of the Association for Computational Linguistics, pp. 1386-1395, 2010.
- B. Pang, L. Lee, and S. Vaithyanathan, ”Thumbs up? sentiment classification using machine learning techniques,” In Proc. of Conf. on Empirical Methods in Natural Language Processing, pp 79-86, 2002.
- T. Mullen and N. Collier, ”Sentiment analysis using support vector machines with diverse information sources,’ In Proc. of Conf. on Empirical Methods in Natural Language Processing,” pp 412–418, 2004.
- Charles E. Osgood, George J. Suci, and Percy H. Tannenbaum. The measurement of meaning, 2nd ed.. University of Illinois Press Urbana, 1967.
- P. D. Turney, “Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews,” In Proc. of the 40th Annual Meeting on Association for Computational Linguistics ACL, pp 417–424, 2002.
- O.F. Zaidan, J. Eisner, and C.D. Piatko, “Using Annotator Rationales to Improve Machine Learning for Text Categorization,” In Proc. of Conf. of North American Chapter of the Association for Computational Linguistics, pp 260–267, 2007.
- Rudy Prabowo and Mike Thelwall,”Sentiment analysis: A combined approach,” Journal of Informetrics,3(2):143–157, 2009.
- Wei-Hao Lin, Theresa Wilson, Janyce Wiebe, and Alexander Hauptmann, ”Which side are you on? identifying perspectives at the document and sentence levels,” In Proceedings of the Conferenceon Natural Language Learning ,2006.
- Hugo Liu., “MontyLingua: An end-to-end natural language processor with common sense”. Technical report,MIT, 2004.
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