Popularity analysis in political election scenario by sentiment classification of polarizing topics on Twitter
Advisor Information
Magie Hall
Location
MBSC 201
Presentation Type
Poster
Start Date
6-3-2020 9:00 AM
End Date
6-3-2020 10:15 AM
Abstract
In a political election scenario, it is required for the political representatives to reach out to the public and convey their policy, roadmaps or any other public manifestos as a political strategy. The use of social media like Twitter, Facebook, YouTube, etc. has been exponentially increased for public reach outs in real-time. Social computing researchers use these platforms to perform research to analyze public opinions and intentions, political agendas, influential efforts, election predictions, etc. Here I am using a topic-centric approach and targeting the statements or topics raised by representatives and publicly most discussed. This includes the method of topic modeling and sentimental analysis on the dataset which will conclude the popularity of political parties and representatives, polarization efforts, influential election topics and the public intentions of voting. In this research, a mixed methodology approach will be performed in an Indian political election 2020 scenario where popularity analysis on the political, their representatives and topics discussed will be done. The research process includes data collection from Twitter, text mining including topic analysis and developing a sentimental classifier using machine learning, and statistical analysis to conclude the results.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Popularity analysis in political election scenario by sentiment classification of polarizing topics on Twitter
MBSC 201
In a political election scenario, it is required for the political representatives to reach out to the public and convey their policy, roadmaps or any other public manifestos as a political strategy. The use of social media like Twitter, Facebook, YouTube, etc. has been exponentially increased for public reach outs in real-time. Social computing researchers use these platforms to perform research to analyze public opinions and intentions, political agendas, influential efforts, election predictions, etc. Here I am using a topic-centric approach and targeting the statements or topics raised by representatives and publicly most discussed. This includes the method of topic modeling and sentimental analysis on the dataset which will conclude the popularity of political parties and representatives, polarization efforts, influential election topics and the public intentions of voting. In this research, a mixed methodology approach will be performed in an Indian political election 2020 scenario where popularity analysis on the political, their representatives and topics discussed will be done. The research process includes data collection from Twitter, text mining including topic analysis and developing a sentimental classifier using machine learning, and statistical analysis to conclude the results.