Abstract
The study was conducted to analyze public opinion about the government's efforts in overcoming the Covid- 19 pandemic by providing social assistance (bansos) in the form of goods, money and or services. Through social media Twitter with the topic of social assistance, the classification of positive, neutral or negative sentiments will be carried out. The results of this classification will reveal what social assistance topics are often discussed on Twitter. This classification process uses the Naive Bayes method and uses the RapidMiner application. The data used in this analysis process is 747 Twitter comment text data with a data collection time span from October to November. The classification process is supported by the Term Frequency-Inverse Document Frequency feature as the word weighting stage. This classification produces 2,382 word attributes or word vectors from 747 data, with 370 sample data for model testing which produces an accuration value of 24.32%, a true neutral recall value of 100%, and a true neutral precision value of 24.32%. The word that most often appears from the results of this sentiment analysis is the word "bantuan".
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