Application of Naïve Bayes Classification to Analyze Performance Using Stopwords
Keywords:
social media, stopwords, naïve bayesAbstract
Based on current data, there has been an increase in social media users, which shows that more and more people are using social media as a place to express themselves and their emotions. This will generate thousands of tweets within a day. The tweet data is processed so that it is useful for stakeholders who need it to help them make a decision. Because sentence structures on social media are often irregular, pre-processing is necessary to make tweet sentences normal. Stemming and Stopwords are pre-processing techniques that are widely used in sentiment analysis. In previous studies, there were indications that its use did not have a significant effect on accuracy. In this study, the authors divide it into four models: using stemming and stopwords and without using stemming and stopwords. Data using stemming gets the best results with an f1-score of 65%. These results indicate an increase in performance in the use of stemming and stopwords using Multi-class Naive Bayes
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