At present, the stage of Internet has changed the way that the people express their views and opinions in Social Media. Millions of people are using social network sites like Facebook, Twitter, Google Plus, etc. to express their emotions, opinion and share views about their daily lives. Through the online communities, sellers get an interactive media where consumers inform and influence others by online forums. Social media is generating a large volume of sentiment rich data especially in the form of tweets, status updates, blog posts, comments, reviews, etc. Sentiment analysis has become a very popular field of research and lot of researchers have explored this field but still, there are many issues as sentiment analysis processes text-based unstructured data. The dictionary-based approach takes less processing time than supervised learning approach but accuracy is not up to the mark. Supervised learning approach provides better accuracy and it is found that sentiment classifiers are severely dependent on domains or topics. In this research paper, applications of sentiment analysis, types of approaches present, and evaluation metrics’ involved in sentiment analysis are discussed briefly.

Data Mining, Sentiment Analysis (SA), Emotion Detection (ED), Opinion Mining, Social Media Network, Applications, Evaluation Metrics

[1]. AkriviKrouska, Christos Troussas, Maria Virvou, “Comparative Evaluation of Algorithms for Sentiment Analysis over Social Networking Services”, Journal of Universal Computer Science, Volume 23, Issue 8, 2017, Pages 755-768.
[2]. Amreen Shaikh, Madhuri Rao, “Survey on Sentiment Analysis”, International Conference on Emanations in Modern Technology and Engineering, Volume 5, Issue 3, 2017, Pages 164 – 167.
[3]. Ankita Gupta, JyotikaPruthi, Neha Sahi, "Sentiment Analysis of Tweets using Machine Learning Approach", International Journal of Computer Science and Mobile Computing, Volume 6, Issue 4, 2017, Pages 444 – 458.
[4]. Anu Sharma and Savleen Kaur, “Techniques for Sentiment Analysis survey”, I J C T A, Volume 41, Issue 9, 2016, Pages 355 - 360.
[5]. Bing Liu. “Sentiment Analysis and Opinion Mining”, Morgan & Claypool Publishers, May 2012.
[6]. Brian Keith, Exequiel Fuentes, and Claudio Meneses. A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews. In Proceedings of ACM SIGKDD Conference, Halifax, Nova Scotia, Canada, 2017, Pages 1 – 10.
[7]. Edison M., A. Aloysius, “Concepts and Methods of Sentiment Analysis on Big Data”, International Journal of Innovative Research in Science, Engineering and Technology, Volume 5, Issue 9, 2016, Pages 16288 - 16296.
[8]. Edison M., A. Flora, D. Vinnarasi, A. Aloysius, “Sentiment Analysis: A Review”, International Journal of Engineering Technology and Computer Research (IJETCR), Volume 5, Issue 4, 2017, Page 26 - 30.
[9]. Govindarajan M., Romina M., “A Survey of Classification Methods and Applications for Sentiment Analysis”, International Journal of Engineering and Science (IJES), Volume 2, Issue 12, 2013, Pages 11-15.
[10]. Jianbo Yuan, Quanzeng You and JieboLuo, “Sentiment Analysis Using Social Multimedia”, Springer International Publishing Switzerland, Multimedia Data Mining and Analytics, 2015, Pages 31 - 59.
[11]. Mahima Gaur and JyotikaPruthi, “A Survey on Sentiment Analysis and Opinion mining”, International Journal of Current Engineering and Technology, Volume 7, Issue 2, 2017, Pages 444 - 446.
[12]. MrunaliOmprakashThakare, MrudulaNimbarte, “User Tracking Using Tweet Segmentation And Word Recognition”, International Conference on Electronics, Communication and Aerospace Technology, 2017, Pages 664 - 668.
[13]. Praveen Kumar, Umesh Chandra Jaiswal, “A Comparative Study on Sentiment Analysis and Opinion Mining”, International Journal of Engineering and Technology (IJET), Volume 8, Issue 2, 2016, Pages 938 - 943.
[14]. Purti Jain, ShrutiKohli, “Big Data Analysis, Algorithms, and Applications: A Survey”, International Journal of Emerging Trends in Engineering Research (IJETER), Volume 3, Issue 1, 2015, Pages 19 – 24.
[15]. Revathi S., N Rajkumar, “A Survey on Sentiment Analysis for Web-Based Data by Using Various Machine Learning Techniques”, International Journal of Engineering Trends and Applications (IJETA) – Volume 1, Issue 2, 2014, Pages 1 - 5.
[16]. Shailesh Kumar Yadhav, “Sentiment Analysis and Classification: A Survey", International Journal of Advanced Research in Computer Science and Management Studies, Volume 3, Issue 3, 2015, Pages 113 - 121.
[17]. Tapan Kumar Hazra, YashikaBihani, Sneha Mishra, “Sentiment Learning Using Twitter Ideograms”, IEEE, 2017, Pages 115 - 120.
[18]. Vidisha M. Pradhan, Jay Vala, PremBalani, “A Survey on Sentiment Analysis Algorithms for Opinion Mining”, International Journal of Computer Applications (0975 – 8887) Volume 133, Issue 9, 2016, Pages 7 - 11.
[19]. Vishal A. Kharde, S.S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications, Volume 139, Issue 11, 2016, Pages 5 - 15.
[20]. Walaa Medhat, Ahmed Hassan, HodaKorashy, “Sentiment analysis algorithms and applications: A survey”, Ain Shams Engineering Journal, Volume 5, 2014, Pages 1093 – 1113.
[21]. Yakshi Sharma, VeenuMangat, Mandeep Kaur, “Sentiment Analysis & Opinion Mining”, International Journal of Soft Computing and Artificial Intelligence, Volume 3, Issue 1, 2015, Pages 59 - 62.