Analisis sentimen binary option trading di twitter menggunakan metode naive bayes classifier

Arifin, Raihan Dimas (2023) Analisis sentimen binary option trading di twitter menggunakan metode naive bayes classifier. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

This study aims to analyze public sentiment towards Binary Option Trading on social media Twitter using the Naive Bayes algorithm. Binary Option Trading is an investment model that has become increasingly popular amidst technological developments, but has also raised concerns regarding fraud and financial risks. Twitter was chosen as the research platform because of its openness to data via API. This research method involves crawling tweet data, data preprocessing, and using the Naive Bayes algorithm for sentiment classification. The results of the study show that public sentiment towards Binary Option Trading tends to be neutral, with a higher number of neutral sentiments compared to positive and negative sentiments. The implementation of the Naive Bayes algorithm in sentiment analysis involves steps such as data collection, data preprocessing, and word probability calculations. System evaluation shows an accuracy of 82.5% with a precision of 83% and a recall of 82%. This research provides insight into public sentiment towards Binary Option Trading on Twitter, as well as provides an understanding of the implementation of the Naive Bayes algorithm in sentiment analysis. The results of this research can be useful for policy holders and decision makers regarding Binary Option Trading, as well as assisting the public in understanding the risks associated with online trading.

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