Sentiment Analysis System for Myanmar News Using Support Vector Machine and Naïve Bayes

Abstract

With the growth of web technology, there are huge amount of information in the web for the internet users. Users not only use that information but also provide opinions for decision making process. Sentiment analysis or opinion mining is one of text categorization techniques that extract opinion expressed in a piece of text. This system created sentiment annotated corpus. Feature extraction and selection are needed in sentiment analysis to get high performance. N-grams are used for feature selection and TF-IDF is used for feature extraction. Machine learning is creating a computer programs that improve performance with experience. Machine learning is combination of the techniques and basis from both statistics and computer science. There are generally three types of machine learning algorithm such as supervised machine learning, unsupervised machine learning and hybrid learning. Supervised machine learning also known as classification algorithm that models the relationships between the feature set and the label set. In this system, Myanmar sentiment analysis system is implemented using supervised machine learning method. This system shows the comparison results of support vector machine (SVM) and Naive Bayes algorithms.