This paper is published in Volume-5, Issue-2, 2019
Area
Social
Author
Mandar Menjoge
Co-authors
Vedant Bhawalkar, Mazhar Sayyad, Jainam Gosaliya
Org/Univ
MIT Polytechnic, Pune, Maharashtra, India
Pub. Date
25 March, 2019
Paper ID
V5I2-1396
Publisher
Keywords
Sentiment analysis, Apache storm framework, Preprocessing, NLP, K-means clustering, Porter stemming algorithm

Citationsacebook

IEEE
Mandar Menjoge, Vedant Bhawalkar, Mazhar Sayyad, Jainam Gosaliya. Twitter sentimental analysis, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Mandar Menjoge, Vedant Bhawalkar, Mazhar Sayyad, Jainam Gosaliya (2019). Twitter sentimental analysis. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Mandar Menjoge, Vedant Bhawalkar, Mazhar Sayyad, Jainam Gosaliya. "Twitter sentimental analysis." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

This paper presents the effectiveness of linguistic features to identify the sentiment of Twitter messages using the apache storm framework. We calculate the effectiveness of present lexical resources and features that capture information about the informal and creative language used in microblogging. In the past few years, there has been a huge growth in the use of microblogging platforms such as Twitter. Influenced by intensification, companies, and media organizations are increasingly seeking ways to excavate Twitter information about what people think and feel about their products and services. Here we download Twitter messages for a particular hashtag and carry out sentiment analysis i.e. to find a positive, negative or neutral sense of that tweet using apache storm framework. Each hashtag may have 1000 of comments and new comments are added every minute, in order to handle so many live tweets we are using apache storm framework.