Open AccessTerm Document Frequency (TDF) Method for Extracting User Posts and Emerging Events in Social Networks
Author(s) -
Siyamalan Manivannan,
M. Iyapparaja,
M. Prasanna,
T. Velmurugan,
Prabhu Jayagopal
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d9496.118419
Subject(s) - curiosity , term (time) , computer science , cluster analysis , data science , social media , world wide web , information retrieval , artificial intelligence , psychology , social psychology , physics , quantum mechanics
Social network is a place where people exchange and share data related to the current trends and events all over the world. This specific behavior of users made us concentrate on the logic that processing these data may lead us to the extracting the current topic of curiosity between the users. Applying data clustering technique like Term-document-Frequency (TDF) based approach over these data may leads us up to the mark but there will be little chance of negatives. We are going to do a likely medium that can give both usual mentioning behaviour of a consumer and also the frequency of users occurring in their mentions. It also works well even the data of the messages are very small information. These extracted emerging topic are shown to the user those who subscribe for the details