Online Social Media Review Mining for Living Items with Probabilistic Approach: A Case Study

• 발행기관: 한국스마트미디어학회
• 저자명: Shuai Li Fei Hao Hee Cheol Kim
• 간행물 정보: 『스마트미디어저널』Vol2, No.2, 20~27쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2013.06.30

영문 초록

The concept of social media is top of the agenda for many business executives and decision makers, as well as consultants try to identify ways where companies can make profitable use of applications such as Netflix, Flixster. The social media is playing an increasingly important role as the information sources for customers making product choices etc. With the flourish of Web 2.0 technology, customer reviews are becoming more and more useful and important information resources for people to save their time and energy on purchasing products that they want. This paper proposes the Bayesian Probabilistic Classification algorithm to mine the social media review, and evaluates it by different splits and cross validation mechanism from the real data set. The explored study experimental results show the robustness and effectiveness of proposed approach for mining the social media review.

목차

Abstract

I. INTRODUCTION

II. PRELIMINARIES AND PROBLEM STATEMENT

III. REVIEW MINING ALGORITHMS ON LIVING ITEMS

IV. EXPERIMENT

V. CONCLUSION

REFERENCES

키워드

Social Media Customer Review User-generated Content Text Mining Laplace Smooth Probabilistic Approach