Topic Evolutionary Tweet Stream Clustering Algorithm and TCV Rank Summarization
Selvaraj K* and Balaji S
Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, India
- *Corresponding Author:
- Selvaraj K
Department of Computer Science and Engineering
Akshaya College of engineering and technology
Tamil Nadu, India 641032
Tel: +91 9578817220
E-mail: [email protected]
Received date: November 04, 2015; Accepted date: May 12, 2016; Published date: May 29, 2016
Citation: Selvaraj K, Balaji S (2016) Topic Evolutionary Tweet Stream Clustering Algorithm and TCV Rank Summarization. Int J Adv Technol 7:162. doi: 10.4172/0976-4860.1000162
Copyright: © 2016 Selvaraj K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Twitter which receives over 400 million tweets per day has emerged as an invaluable source of news, blogs, opinions and more. Our proposed work consist three components tweet stream clustering to cluster tweet using kmeans cluster algorithm and second tweet cluster vector technique to generate rank summarization using greedy algorithm, therefore requires functionality which significantly differ from traditional summarization. In general, tweet summarization and third to detect and monitors the summary-based and volume based variation to produce timeline automatically from tweet stream. Implementing continuous tweet stream reducing a text document is however not a simple task, since a huge number of tweets are worthless, unrelated and raucous in nature, due to the social nature of tweeting. Further, tweets are strongly correlated with their posted instance and up-to-the-minute tweets tend to arrive at a very fast rate. Efficiency-tweet streams are always very big in level, hence the summarization algorithm should be greatly capable. Flexibility-it should provide tweet summaries of random moment durations. Topic evolution-it should routinely detect sub-topic changes and the moments that they happen.