A Performance Evaluation of Sentiment Classification Applying SVM, KNN, and Naive Bayes
Md Deloar Hossan Jasy, Sakib Al Hasan, Md Ibrahim Khalil Sagor, Abdullah Al Noman, Jiang Ming Ji · 2021
The rising use of the internet and social networks has opened up new avenues for individuals to express themselves. It’s also a platform with a plethora of information where an individual can see other people’s thoughts, which are diverged into numerous sentiment categories and are slowly becoming a primary part of the decision. This study makes a significant contribution to sentiment classification, which is effective in determining data in a big amount of tweets with de-contextualized sentiments which are often positive or negative, or in the middle. To accomplish this, we initially pre-processed the raw data, and then draw out the meaningful words and phrases (characteristic vector), then picked the characteristic vector list, and then applied machine-learning classification methods including Naive Bayes, KNN, and SVM. And at last, we assessed the classifier’s performance using the terms recall, accuracy, and precision, as well as the F1-score. Support Vector Machine has the highest accuracy of 92 percent, followed by KNN and Naive Bayes with 88 and 85 percent accuracy, respectively.