Sentiment Analysis as Assessment of the COVID-19 Social Assistance Pollemic using Random Forest Algorithm

The government’s endeavors in organizing the COVID-19 Social Assistance program often encounter problems and lead to the opinion of many parties. One of the opinions expressed on social media is twitter. Sentiments from these opinions were then analyzed to find out the assessment and discussion of each sentiment that can be used as evaluation material for the Social Assistance program. In this study, the sentiment of each preprocessed text was obtained using a labeling process with an assessment of polarity and subjectivity from TextBlob library. The results of neutral, positive, and negative sentiment assessments were weighted using TFIDF. Words that have been formatted into numeric then classified using the Random Forest algorithm. The parameters in this case were in accordance with the documentation on sklearn. An evaluation of the algorithm was also carried out using the 10 kfold cross validation method as a performance validation of the results of testing each piece of data. The performance obtained is quite satisfactory.