Sentiment Analysis on KAI Twitter Post Using Multiclass Support Vector Machine (SVM)

Abstract

Information in form of unstructured texts is increasing and becoming commonplace for its existence on the internet. This information is easily found and utilized by business people or companies through social media. One of them is Twitter. Twitter is ranked 6th as a social media that is widely accessed today. The use of Twitter has the disadvantage of unstructured and large data. Consequently, it is difficult for business people or companies to know opinion towards service with limited resources. To Make it easier for businesses know the public's sentiment for better service in the future, public sentiment on Twitter needs to be classified as positive, neutral, and negative. The Multiclass Support Vector Machine (SVM) method is a supervised learning classification method that handles three classes classification. This paper uses One Against All (OAA) approach as a method to determine the class. This paper contains the results of classifying OAA Multiclass SVM methods with five different weighting features unigram, bigram, trigram, unigram+ bigram, and word cloud for analyzing tweet data, finding the best accuracy and important feature when processed with large data. The highest accuracy is the unigram TF-IDF model combined with the OAA Multiclass SVM with gamma 0.7 is 80.59.