Shihab Elbagir
ABSTRACT
Twitter is a popular microblogging platform that provides a tremendous amount of data, which can be used for sentiment analysis. Accordingly, numerous existing studies have concentrated on social media and sentiment analysis. The current study presents an approach to classify tweets into ordinal classes concerning a topic. Real-time tweets related to the 2016 US presidential election are gathered using Node.xl. Thereafter, these tweets are preprocessed and classified using Python and Valence Aware Dictionary for sEntiment Reasoner (VADER). Experimental result reveals that the proposed approach can perform a good results in detecting multi-sentiment classification.
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Index Terms
- Sentiment Analysis of Twitter Data based on Ordinal Classification
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