APPLYING MACHINE LEARNING TO CLASSIFY SENTIMENT TEXT FOR VIETNAMESE LANGUAGE ON SOCIAL NETWORK DATA

다국어 초록 (Multilingual Abstract)

Since the government issued ICT priority policy for the last decade, Vietnam was reported to have impressed development of ICT infrastructure and Internet users. Until May of 2015, Vietnam has 39.7 millions of Internet users and 31 millions of social network user accounts. Facebook is the dominant website with more than 22 million Vietnamese users and 70% of those accesses Facebook via mobile phone. Several companies have utilized Facebook as the most effective interaction channels. The increasing of big text data such as posts and comments on Facebook that embed customer opinions requires method to mine sentiment text in Vietnamese language. This research applies machine learning with several algorithms such Naive-Bayes, decision trees and Support Vector Machine (SVM) for Vietnamese text data collected from fast-food industry on Facebook. The experiment results show that machine learning methods are able to classify Vietnamese sentiment text with the accuracy over 70%. Thus we proposed several recommendations for mining Vietnamese social text data.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related work
• 3. Research methods
• 3.1 Data collection
• 3.2 Preprocessing steps
• 3.3. Machine learning algorithms
• 4. Experiment results
• 5. Discussion and conclusions
• References