Sentiment Analysis of Online Lectures Tweets using Naïve Bayes Classifier

Abstract

Online lecture is an alternative learning method during the Covid-19 pandemic. There are opinions with pro and contra of the learning method. The purpose of this study is to evaluate the tweets of opinion or sentiment retrieved from social media Twitter regarding online lectures among the Indonesian community. Twint is used to collect the data tweet and Jupyter notebook is for text preprocessing and classification. The processes started with scraping data from Twitter, text preprocessing, and text classification. Using the Naïve Bayes classifier shows the performance has a precision value of 100%, an accuracy value of 70.8%, an F-measure of 10.2%, and a recall value of 5.4%. Performance rating can be affected by the dataset used for modeling. This analysis covers the positive sentiment and negative sentiments toward online lectures and the result shows 69% negative sentiments and 31% positive sentiments. The negative sentiments had a higher percentage compared to positive sentiments. The results were also supported by the word cloud which expressed a high frequency of negative words such as sleep problems, bored, tired, dizzy, difficult and lazy. So, it is concluded that during the Covid-19 pandemic from August 1, 2020, to May 31, 2021, Twitter users in Indonesia had negative sentiments about online lectures.