Twitter sentiment analysis using support vector machine and deep learning model in e-learning implementation during the Covid-19 outbreak

Investigating the effectiveness of using e-learning during the Covid-19 outbreak around the world is very interesting. This can be done by mining public opinion data on the application of e-learning during the Covid-19 outbreak. Twitter Sentiment Analysis is one techniques that can be used by classifying tweet data related to public opinion and classifying it into positive and negative sentiments, with the aim of seeing how public sentiment is related to the application of e-learning to help the government in taking a policy. The stages of research carried out in this study include Data Collection, Data Pre-processing (Tokenizing, Trans-form Case, Stopword Filter, Generate N-Gram and Stemming), Use of Models or Methods such as Support Vector Machine (SVM) algorithm and Deep Learning models, Experiment and Model Assessment using Rapid Miner version 9.9, and Evaluation and Validation Results using Confusion Matrix and ROC Curves. Based on 444 tweet data in English with the keywords #elearningcovid19, #elearning and #covid19, the results of the accuracy and AUC values of the SVM algorithm were superior to the Deep Learning model, namely 90,53% and 87,16%, as well as the AUC value to 0,953 and 0,928. Based on the research results, it turns out that more people in the world agree with the application of e-learning during the Covid-19 outbreak.