Abstract:
Sentiment Analysis (SA) is also known as opinion mining. The fundamental
task of Sentiment Analysis (SA) is to extract, identify and determine the subjective
information that is social sentiment in the source text. The application of Sentiment
Analysis found in analyzing opinion to classify the class of a document or collection
of documents, like blog posts, reviews, news articles and social media feeds like tweets
and status updates. This study presents the idea of Sentiment Analysis in term of
comparison on three different machine learning techniques, and illustrates how
Myanmar Facebook comments are classified by applying word vector representation
techniques. In this work, Facebook comments are used for model training and testing.
They are collected from Myanmar Celebrity page. These comments are transformed as
word vector by using word vector representation techniques – TF-IDF, Word2Vec and
Pre-trained Word2Vec. Then, the word vector are trained with three machine learning
classifiers for sentiment identification. Three machine learning classifiers are Logistic
Regression, Support Vector Machine (SVM) and Random Forest. According to the
experimental results of Pre-trained Word2Vec outperformed other two methods of
word vector representation technique.
