Earticle

초록

영어

With the rapid development of the Internet, the number of Internet users has increased significantly. More and more viewers tend to publish their opinions and comments on movies in blogs, forums, Weibo, and online videos. This kind of large-scale spontaneous online movie commentary, by its unique diversity and universality, has become an important reference factor for studying the popularity of movies in the public and evaluating the pros and cons of movies. However, the user comment data of e-commerce platforms such as Weibo is often huge and vulnerable to the socio-economic environment, showing a large scale, dynamic and complex. How to analyze users' interests and preferences from massive and text-type comments, extract topics of interest to users, and satisfy and dissatisfied goods and their attributes, and become a new era of e-commerce enterprises to improve the quality of goods and services, grasp the social prevalence Trends, as well as fundamental, critical issues that must be addressed and addressed by precision marketers. Based on this, I decided to use statistical methods to empirically analyze the characteristics of the review data and conduct data mining, and use neural network learning, artificial intelligence and other techniques to analyze user sentiment and user preferences/interests in the review data. The user sentiment analysis and user preference/interest mining in the comment data are targeted for the two consumer behavior analysis tasks, and the user's interest network will be extracted to make the recommendation system of interest to the users.

목차

Abstract
1. Introduction
1.1. The rapid development of e-commerce generates massive data
1.2. Problem statement
1.3. Research purposes
1.4. Research significance
2. Research Background
2.1. Traditional User Sentiment Analysis and Interest Mining Method
2.2. Vectorized representation of the comment text
2.3. User Interest Community and RecommendationSystem
2.4. Inadequacies in existing research and newchallenges
3. Research Method
References

키워드

저자

• Li Zhipeng [ PH.D Chonnam National University ]
• Park Seungbong [ Professor Chonnam National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658