2026-05-01T04:10:53Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/00006846 2022-03-24T23:11:50Z 1582963342780:1596102355557 user-uit

Annotation and Sentiment Analysis System for Myanmar News Using Naïve Bayes Algorithm Thein Yu Khin Thandar Nwet May Kyi Nyein Sentiment analysis or opinion mining is a combination technique of computational linguistics, natural language processing, and text analytics. Sentiment analysis provides important pieces of subjective information during decision making process. News provides very important information for people. There are many sentiment analysis researches for English and other language, but there is a little sentiment analysis research for Myanmar language. In Myanmar language, there are challenges in scare of resources. We construct Myanmar news sentiment tagged corpus and news data are collected from many websites such as duwun.com, news-eleven.com, 7daydaily.com and popularmyanmar.com, ayeyarwady.com and so on. In developing sentiment analysis, identification and extraction of feature information in source domain are also needed to perform. We use N-gram feature extraction process in this research to get more performance. Naïve Bayes is very similar and intuitive classification algorithm. In this paper, statistical approach such as Naïve Bayes algorithm is applied in document level sentiment analysis. We implement this system using Python Jupyter Notebook. We use 600 news data set including 300 positive news and 300 news positive news. 2019-06-25 http://hdl.handle.net/20.500.12678/0000006846 https://meral.edu.mm/records/6846