Natural Language Processing-based Random Forest Classifier for Fake and Genuine Tweet Detection with Polarity Score

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Smita Khond, B. Sai Sruthi, A. Manisha Kumari, B. Vaishnavi, Ch. Chandana Priya


Lexicon algorithm, which is a natural language processing (NLP) technique is used to determine the sentiment expressed by a textual content. This sentiment might be negative, neutral, or positive. It is possible to be sarcastic using only positive or neutral sentiment textual contents. Hence, lexicon algorithm can be useful but yet insufficient for sarcasm detection. It is necessary to extend the lexicon algorithm in order to come up with systems that would be proven efficient for sarcasm detection on neutral and positive sentiment textual contents. In this project, two sarcasm analysis systems both obtained from the extension of the lexicon algorithm have been proposed for that sake. In addition, this work also utilizes the decision tree concept to find the polarity for fake and genuine tweets. The first system consists of the combination of a lexicon algorithm and a pure sarcasm analysis algorithm. The second system consists of the combination of a lexicon algorithm and a sentiment prediction algorithm.

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