People Opinion Topic Model: Opinion based User Clustering in Social Networks

نویسندگان

  • Hongxu Chen
  • Hongzhi Yin
  • Xue Li
  • Meng Wang
  • Weitong Chen
  • Tong Chen
چکیده

Mining various hot discussed topics and corresponding opinions from different groups of people in social media (e.g., Twitter) is very useful. For example, a decision maker in a company wants to know how different groups of people (customers, staff, competitors, etc.) think about their services, facilities, and things happened around. In this paper, we are focusing on the problem of finding opinion variations based on different groups of people and introducing the concept of opinion based community detection. Further, we also introduce a generative graphic model, namely People Opinion Topic (POT) model, which detects social communities, associated hot discussed topics, and perform sentiment analysis simultaneously by modelling user’s social connections, common interests, and opinions in a unified way. This paper is the first attempt to study community and opinion mining together. Compared with traditional social communities detection, the detected communities by POT model are more interpretable and meaningful. In addition, we further analyse how diverse opinions distributed and propagated among various social communities. Experiments on real twitter dataset indicate our model is effective.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Viral Marketing in an Online Discussion Forum

Online opinion leaders play an important role in the dissemination of information in discussion forums. They are a high-priority target group for viral marketing campaigns. On an average, an opinion leader will tell about his or her experience with a product or company to 14 other people. It is important to identify such opinion leaders from data derived from online activity of users. We presen...

متن کامل

Analysis of Users’ Opinions about Reasons for Divorce

One of the most important issues related to knowledge discovery is the field of comment mining. Opinion mining is a tool through which the opinions of people who comment about a specific issue can be evaluated in order to achieve some interesting results. This is a subset of data mining. Opinion mining can be improved using the data mining algorithms. One of the important parts of opinion minin...

متن کامل

Probabilistic User-Level Opinion Detection on Online Social Networks

The mass popularity of online social networks such as Facebook and Twitter makes them an interesting and important platform for exchange of ideas and opinions. Accurately capturing the opinions of users from their self-generated data is crucial for understanding these opinion flow processes. We propose a supervised model that uses a combination of hashtags and n-grams as features to identify th...

متن کامل

A Competent Approach for Extracting and Visualizing Web Opinions Using Clustering

Huge amount of Web opinions are available in the social sites due to the development of web Communication. Web opinion acts as a boundary between the Web users and I n t e r n e t . It allows t h e users to communicate and articulate their opinions without eye to eye contact. Nevertheless the clustering and visualizing of the web opinion is a not a trouble-free task. The obtainable Document Clu...

متن کامل

Topic-Level Opinion Influence Model(TOIM): An Investigation Using Tencent Micro-Blogging

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging turns out to be difficult, since text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate each other to form a social network, we hypothesize that user opinion is influenced by its neighbors i...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017