A Survey of Various Opinion Mining in Social Media
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
Download this PDF format
Opinion Mining is a technique of providing and giving opinion on a particular topic so that a final conclusion can be extracted from it. Here in this paper a complete survey of all the technique that is used for the opinion mining. A complete survey of all the technique implemented for the social media opinion is discussed and analyzed here so that various advantages and limitations can be analyzed and hence on the basis of which a new and efficient technique can be implemented in future
 Laxmi Choudhary and Bhawani Shankar Burdak “Role of Ranking Algorithms for Information Retrieval”, 2010
 Nikhil Sanyog Choudhary, Himanshu Yadav and Anurag Jain “Message Filtering Techniques in Social Networks over Web Environment- A Survey”, IJETAE, 2014.
 Liaoruo Wang, Tiancheng Lou, Jie Tang and John E. Hopcroft “Detecting Community Kernels in Large Social Networks”, 2011
 Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee-Peng Lim, Hongfei Yan and Xiaoming Li, “Comparing Twitter and Traditional Media using Topic Models”, 2011
 Hila Becker, Mor Naaman and Luis Gravano “Selecting Quality Twitter Content for Events”, Association for the Advancement of Artificial Intelligence, 2011.
 Liangjie Hong, Ovidiu Dan and Brian D. Davison “Predicting Popular Messages in Twitter”, ACM, 2011.
 Roja Bandari, Sitaram Asur and Bernardo Huberman, “The Pulse of News in So cial Media: Forecasting Popularity”, 2011.
 Phil Long and George Siemens “Penetrating the Fog: Analytics in Learning and Education”, Educause Review, 2011.
 Mattias Rost, Louise Barkhuus, Henriette Cramer and Barry Brown “Representation and Communication: Challenges in Interpreting Large Social Media Datasets”, ACM, 2013.
 Xin Chen, Mihaela Vorvoreanu and Krishna Madhavan, “Mining Social Media Data for Understanding Students’ Learning Experiences”, IEEE Transactions, 2014
World Wide Web, Opinion Mining, WCM, WUM, Social Network.