Twitter text mining for sentiment analysis on government’s response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm

Abstract Opinions about the government’s response to forest fires have drawn many opinions from the community. One way for people to express their opinions is to use social media Twitter. This study conducted a sentiment analysis process on the government’s response to handling forest fires in Indonesia in 2019 with data sources from Twitter. The analysis was carried out on 6325 datasets written on Twitter on September 20, 2019, and then through the process of pre-processing, automating labeling and classification. The automate labeling process uses a Vader that automatically detects the negative or positive polarity of each data and then goes through the classification process using the KNN algorithm. The test results that were built using rapidminer tools showed an accuracy level of the KNN algorithm of 79.45%, the highest if compared to other classifier algorithms such as decision trees, naïve Bayes and random forests. The sentiment analysis process can almost run automatically without human touch because there is already automated labeling using Vader. Testing sentiment analysis related to the government’s response to forest fires can be analyzed using the KNN algorithm and lexicon polarity detection Vader can be done properly.