Mr.N.Vignesh1, Ms.P.Salomi2
1PG student- Department of CSE - Vellammal college of
Engineering and technology, Madurai, Tamilnadu, India.
2Assistant Professor - Department of CSE- Vellammal
college of Engineering and technology, Madurai, Tamilnadu,
India.
Communication
over social networks, such as Facebook and Twitter, is gaining its importance
in our daily life. Since the information exchanged over social networks is not
only texts but also URLs, images, and videos, they are challenging test beds
for the study of data mining. Our basic assumption is that a new (emerging)
topic is something people feel like discussing, commenting, or forwarding the
information further to their friends. Conventional approaches for topic
detection have mainly been concerned with the frequencies of (textual) words. A
term-frequency-based approach could suffer from the ambiguity caused by
synonyms or homonyms. In the Existing System, they propose a probability model
of the mentioning behaviour of a social network user, and propose to detect the
emergence of a new topic from the anomalies measured through the model.
Aggregating anomaly scores from hundreds of users, they show that they can
detect emerging topics only based on the reply/mention relationships in
social-network posts. They demonstrate their technique in several real data
sets they gathered from Twitter. In some cases much earlier when the topic is
poorly identified by the textual contents in posts.For this, we enhance the
existing work by combining the existing link-anomaly model with text-based
approaches. Because the existing link-anomaly model does not immediately tell
what the anomaly is. Combination of the word-based approach with the
link-anomaly model would benefit both from the performance of the mention model
and the intuitiveness of the word-based approach. It is implemented by using
the technique text based change-point detection method for word based approach.
