Inferring Political Preferences from Twitter
- URL: http://arxiv.org/abs/2007.10604v1
- Date: Tue, 21 Jul 2020 05:20:43 GMT
- Title: Inferring Political Preferences from Twitter
- Authors: Mohd Zeeshan Ansari, Areesha Fatima Siddiqui and Mohammad Anas
- Abstract summary: Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate.
During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians.
In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Sentiment analysis is the task of automatic analysis of opinions and emotions
of users towards an entity or some aspect of that entity. Political Sentiment
Analysis of social media helps the political strategists to scrutinize the
performance of a party or candidate and improvise their weaknesses far before
the actual elections. During the time of elections, the social networks get
flooded with blogs, chats, debates and discussions about the prospects of
political parties and politicians. The amount of data generated is much large
to study, analyze and draw inferences using the latest techniques. Twitter is
one of the most popular social media platforms enables us to perform
domain-specific data preparation. In this work, we chose to identify the
inclination of political opinions present in Tweets by modelling it as a text
classification problem using classical machine learning. The tweets related to
the Delhi Elections in 2020 are extracted and employed for the task. Among the
several algorithms, we observe that Support Vector Machines portrays the best
performance.
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