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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Research Journal of Modernization in Engineering Technology and Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
