Abstract:
This paper presents a study of comparison on
three different machine learning techniques to
sentiment analysis for Myanmar language. The
fundamental part of sentiment analysis (SA) is to
extract and identify the subjective information that is
social sentiment in the source text. The sentiment class
is positive, neutral or negative of a comment. The
experiments are done on the collected 10,000
Facebook comments in Myanmar language. The
objective of this study is to increase the accuracy of
the sentiment identification by using the concept of
word embeddings. Word2Vec is used to train for
producing high-dimensional word vectors that learns
the syntactic and semantic of word. The resulting word
vectors train Machine Learning algorithms in the form
of classifiers for sentiment identification. This
experimental results prove that the use of word
embeddings from the collected real world datasets
improved the accuracy of sentiments classification
and Logistic Regression outperformed the other two
ML methods in terms of accuracy and F-measures.
