Analisis Sentimen Terhadap Cryptocurrency Berbasis Python TextBlob Menggunakan Algoritma Naïve Bayes - Azhar, Rizaldi; Surahman, Adi; Juliane, Christina (STIKOM Tunas Bangsa Pematangsiantar, 2022)
Cryptocurrency users are now increasing as the market becomes more and more attractive. In 2019 recorded around 139 million account users verified id cryptocurrency. Recently, it was enlivened by the emergence of #crypto on Twitter and had become a world trending topic. This gives rise to many opinions and opinions from twitter users. With so many twitter users' opinions on the hashtag, it is very difficult to know whether positive, negative or neutral sentiments are manual. This requires machine learning to be able to automate labeling, be it positive, neutral or negative sentiments. Machine learning used is by utilizing Python TextBlob. The results of automatic labeling using Python TextBlob from a total of 1032 tweets obtained 632 tweets or 61.24% containing positive sentiments, 296 neutral sentiments or 28.68% tweets and 104 negative sentiments or 10.07%. The test results using the Naïve Bayes algorithm with each testing data and training data are 0.2 and 0.8. From this test, the accuracy value is 71.98%, precision is 83.04%, recall is 60.88% and f1_score is 65.07%.