文章基本信息

标题：A FRAMEWORK FOR SEMENTIC LEVEL SOCIAL SENTIMENT ANALYSIS MODEL
本地全文：下载
作者：MADHU BALA MYNENI ; L V NARASIMHA PRASAD ; J SIRISHA DEVI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：16
页码：4049
出版社：Journal of Theoretical and Applied
摘要：Social sentiment analysis is playing a vital role in analytics applications like product assessments, people opinions on sudden events and disaster assessments etc. Now the current research is focusing on dynamic big data analysis. The rich sources of dynamic data are twitter, face book, linkedln, snapchat, instagram, reddit and e-commerce web resources. In this paper the importance of semantic level social sentiment analysis with issues, tools and algorithms and machine learning algorithms role are discussed. A case study on Indian railway passenger tweets analysis is discussed and finds the sentiment of passengers on railway services.
关键词：Social Sentiment; Machine Learning; Text Processing