This paper is published in Volume-2, Issue-1, 2016
Area
Data Mining
Author
Aditya Shirke
Co-authors
Ankur Singh, Umesh Sapar, Aditya Sengupta, Prof. Leena Deshpande
Org/Univ
Vishwakarma Institute of Information Technology, Pune, India
Paper ID
V2I1-1136
Publisher

Citationsacebook

IEEE
Aditya Shirke, Ankur Singh, Umesh Sapar, Aditya Sengupta, Prof. Leena Deshpande. Streaming Analytics, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Aditya Shirke, Ankur Singh, Umesh Sapar, Aditya Sengupta, Prof. Leena Deshpande (2016). Streaming Analytics. International Journal of Advance Research, Ideas and Innovations in Technology, 2(1) www.IJARIIT.com.

MLA
Aditya Shirke, Ankur Singh, Umesh Sapar, Aditya Sengupta, Prof. Leena Deshpande. "Streaming Analytics." International Journal of Advance Research, Ideas and Innovations in Technology 2.1 (2016). www.IJARIIT.com.

Abstract

Nowadays Sentiment Analysis play an important Role in each field such as Stock market, product reviews, news article, political debates which help us to determining current trend in the market regarding specific product, event, issues. Here we are apply sentiment analysis on micro-blogging platforms such as twitter, Facebook which is used by different people to express their opinion with respect to different kind of foods in the field of home’s chef. This paper explain different methods of text pre-processing and applies them with a naive Bayes classifier in a big data, distributed computing platform with the goal of creating a scalable sentiment analysis solution that can classify text into positive or negative categories. We apply negation handling, word n-grams, stemming, and feature selection to evaluate how different combinations of these pre-processing methods affect performance and efficiency.
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