Project #15
Title: Topic modeling by scraping social networks
Leader's Name: Rafel Ridha
Member2 Name: Sam Sam
Related paper: Shuo Chen, Ju Fan, Guoliang Li, Jianhua Feng, Kian-lee Tan, Jinhui Tang , Online Topic-Aware Influence Maximization. Proceedings of the VLDB Endowment, Vol. 8, No. 6
Presentation Day: 25 May
Model: LE
Abstract:
Topic modeling in social networks has got a lot of attention recently
due to the usage in marketing. More importantly, influence
maximization has been a huge contributor for viral marketing and
advertising. This could be done by either using the APIs provided by
the social network services (if the service provide such a thing) or
scraping data from a social network and make a post-analysis of the
data for topics. The result of the analysis can of course be used in
many interesting subjects such as marketing, social behaviour and
others.
The goal of this project is to use an efficiency way to store scrapped
data from social communities and cluster them in regards to topics and
their popularity within the social networks. Meaning, you can present
users connected to topics and the strength of the connection to that
topic.