Sentiment Analysis Twitter Based Lexicon and Multilayer Perceptron Algorithm

Abstract

As the COVID-19 pandemic begins, the perception of online lectures according to students needs to be researched, to find out whether students have positive or negative sentiments regarding online lectures so far. Therefore, it is necessary to conduct research on sentiment analysis about online lectures taken according to student comments via tweets on the Twitter platform. The extracted tweets data will then be analyzed using machine learning to predict student sentiment about online lectures. The multilayer perceptron algorithm is used in research because it can solve non-linear problems well and is easy to implement without complicated parameter settings. However, multilayer perceptron is a supervised learning algorithm so it requires data that has been labeled/classified. So that to label the data of online lecture tweets, lexicon-based sentiment analysis is used. A total of 2,391 Indonesian-language tweets were successfully extracted. The results of the study using lexicon-based showed that as many as 63.9% gave negative sentiments towards online lectures, and 29% gave positive sentiments while the remaining 7.1% gave neutral sentiments. Meanwhile, the prediction ability of the multilayer perceptron algorithm for tweets data in this online lecture produces an accuracy of 71%.