Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

LDA-based model for topic evolution mining on text

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)
Qingqiang Wu ; Software Sch., Xiamen Univ., Xiamen, China ; Caidong Zhang ; Xiang Deng ; Changlong Jiang

A text mining model for topical evolutionary analysis was proposed through a text latent semantic analysis process on textual data. Analyzing topic evolution through tracking the topic different trends over time. Using the LDA model for the corpus and text to get the topics, and then using Clarity algorithm to measure the similarity of topics in order to identify topic mutation and discover the topic hidden in the text. Experiments show that the proposed model can discover meaningful topical evolution.

Published in:

Computer Science & Education (ICCSE), 2011 6th International Conference on

Date of Conference:

3-5 Aug. 2011