Annotation and Sentiment Analysis for Myanmar News

Abstract

This paper presents a corpus of Myanmar News and its metadata, annotated with sentiment polarity. It contains 100 news from Myanmar media websites such as news-eleven.com, 7daydaily.com and popularmyanmar.com. This news dataset is of at most value as all the annotation is done manually and this makes it a very rich dataset for training purposes. In this work, we describe the creation and annotation process, content, and the possible uses of the dataset. As an experiment, we have built a basic classification system to identify the emotion polarity of the news. N-gram feature selection algorithm is used in this system to select the most relevant features from training dataset. In Experiment, we classify the News by using well-known Naïve Bayes classifier.