Sentiment analysis tweet online loans using naïve bayes algorithm

Abstract

Online loans are an attractive financial alternative for people who need funds urgently because they are effortless and efficient. Public opinion about online loans continues to appear on social media platforms, one of which is Twitter. Tweets about online loans gave rise to all kinds of positive and negative opinions. Sentiment analysis is a technique of understanding and identifying opinions from a dataset in text form to produce positive and negative sentiments. In this study, the dataset was taken from tweets on Twitter with the keyword #pinjol and produced 6000 data, consisting of 5677 tweets with negative sentiment and 1230 tweets with positive sentiment. In the classification using the Naïve Bayes algorithm with weighting using the TF-IDF method, this algorithm will be compared with the K-Nearest Neighbors algorithm as a performance comparison in classifying sentiment analysis. The Naïve Bayes method shows a slightly lower accuracy of 99.71% compared to KNN, with an accuracy value of 99.78% at a ratio of 20:80. Meanwhile, in the ratio of 30:70, the Naïve Bayes method shows the best accuracy, which is 99.71% compared to KNN with an accuracy value of 99.61%.