Mining social network for extracting topic of textual conversations

Authors:
Pooya Moradian Zadeh;Maryam Mohi;Mohsen Sadighi Moshkenani
Affiliations:
Sharif University of Technology, Kish campus, Iran;Sharif University of Technology, Kish campus, Iran;Sharif University of Technology, Kish campus, Iran
Venue:
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Year:
2008

Citing 9
Cited 0

Mining the network value of customers

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Social Networks Applied

IEEE Intelligent Systems
Social Network Discovery by Mining Spatio-Temporal Events

Computational & Mathematical Organization Theory
Mining Social Networks for Targeted Advertising

HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Mining email social networks

Proceedings of the 2006 international workshop on Mining software repositories
A Semantic Topic Identification System for Document Retrieval on the Web

WAIMW '06 Proceedings of the Seventh International Conference on Web-Age Information Management Workshops
Different Aspects of Social Network Analysis

WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Discovering Temporal Communities from Social Network Documents

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Developing internet usage and services urged play strong role for social network. Social networks are environment which uses internet as interface to provide relations between people, in the other word to interchange data and information between persons. Email and Instant Messengers are popular examples of them. Whereas these environments are continuously and instantly developing, revising and viewing by humans, they are good places for mining. In this paper, the topic of exchanged information between users in this type of networks will be our target. Our method is to use a hierarchical dictionary of semantically related topics and words that is mapped to a graph. Then extracted keywords from context of social network area compared to graph nodes and the dependency between them will be computed. This model can be used in many applications such as marketing, advertising and high-risk group detection.