Social media accounts like blogs, Facebook, Twitter and online discussion sites provide an option for an individual to express his or her opinion. These opinions are usually unstructured data and these are huge in amount. These days a massive number of users collect these recommendations or reviews for products and services, based on which they make their choices. The process of extraction of this insight from unstructured web data can be handled by Natural Language Processing and Big Data Analytics techniques. In this paper, we propose a model to extract this unstructured data from various domains, and then convert it into structured format by using various supervised algorithms. Finally the opinions or sentiments of the users will be presented for further understanding. Based on which the organization can take the necessary step to improve the customer retention.

Sentiment Analysis, Natural Language Processing, Unstructured data, Opinion Mining

[1] Zolbanin, Hamed M., and Dursun Delen. "Processing electronic medical records to improve predictive analytics outcomes for hospital readmissions." Decision Support Systems (2018).
[2] De Heer J., Porskamp P. (2019) Predictive Analytics for Leadership Assessment. In: Kantola J., Nazir S., Barath T. (eds) Advances in Human Factors, Business Management and Society. AHFE 2018. Advances in Intelligent Systems and Computing, vol 783. Springer, Cham
[3] Peng K, Cooke J, Crockett A, Shin D, Foster A, Rue J, Williams R, Valeiras J, Scherer W, Tuttle C, Adams S. Predictive analytics for University of Virginia football recruiting. InSystems and Information Engineering Design Symposium (SIEDS), 2018 2018 Apr 27 (pp. 243-248). IEEE.
[4] Dabbas, Mohammad, Perambur S. Neelakanta, and Dolores DeGroff. "ANN-based predictive analytics of forecasting with sparse data: Applications in data mining contexts." In Recent Trends in Information Technology (ICRTIT), 2013 International Conference on, pp. 62-67. IEEE, 2013.
[5] Martens, David, Foster Provost, Jessica Clark, and Enric Junqué de Fortuny. "Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics." MIS quarterly 40, no. 4 (2016).
[6] Chennamsetty, Haritha, Suresh Chalasani, and Derek Riley. "Predictive analytics on Electronic Health Records (EHRs) using Hadoop and Hive." In Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on, pp. 1-5. IEEE, 2015.
Riensche, Roderick M., Patrick R. Paulson, Gary Danielson, Stephen D. Unwin, Scott Butner, Sarah Miller, Lyndsey Franklin, and Nino Zuljevic. "Serious Gaming for Predictive Analytics." In AAAI Spring Symposium: Technosocial Predictive Analytics, pp. 108-113. 2009.
[8] Huang, Zhenyu, Pak Chung Wong, Patrick Mackey, Yousu Chen, Jian Ma, Kevin Schneider, and Frank L. Greitzer. "Managing Complex Network Operation with Predictive Analytics." In AAAI Spring Symposium: Technosocial Predictive Analytics, pp. 59-65. 2009.
[9] M.P.S. Bhatia and Deepika Khurana, “ Experimental Study of Data Clustering using k-means and Modified Algorithms”, International Journal of Data Mining and Knowledge Management Process(IJDKP), Volume 3, No. 3, May-2013.
[10] Srihari A.Hudli, Aditi A.Hudli, Ananad V.Hudli, “ Identifying Online Opinion Leaders Using K-means Clustering”, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA).
[11] J.V.N. Lakshmi, Ananthi Sheshasaayee, "A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.92-97, 2017
[12] R.Anupriya, P.Saranya, R.Deepika, "Mining Health Data in Multimodal Data Series for Disease Prediction", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.96-99, 2018