Earticle

초록

영어

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

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

키워드

저자

• Hoanh-Su Le [ Candidate Ph. D., Graduate School of Business, Pukyong National University, S. Korea ]
• Jong-Hwa Lee [ Candidate Ph. D., Graduate School of Business, Pukyong National University, S. Korea ]
• Hyun-Kyu Lee [ Professor. College of Business Administration, Pukyong National University, S. Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658