A INNOVATIVE HOT TOPIC DETECTION SCHEMA WITH SYNTHESIS OF IMAGE AND SHORT TEXT KNOWLEDGE RETRIEVAL FROM TWITTER

Abstract

In recent years, huge amount of stored information has been enormously increasing day by day which is generally in the unstructured form and cannot be used for any processing to extract useful information. Exploring potentially useful information from huge amount of textual data produced by social networking services has attracted much attention in recent years.  In Micro-blogging web services such as Twitter, the user is often bombarded with tons of information and raw data, with user unable to classify it into right category. The solution to overcome this problem can be derived from automatic text classification process. Twitter exhibits several characteristics, including a limited number of features and noisy text information. Extracting valuable information from Twitter has made hot topic detection a challenging task. In this paper, a novel four-stage framework is proposed to improve the performance of topic detection. Data preprocessing is the first stage. Deep learning is then exploited to enrich short text information via image understanding. Next, improved latent Dirichlet allocation is used to optimize the image effective word pairs, which improves the accuracy of the extracted topic words. Finally, both short text and images are integrated for topic detection, in which the corresponding topics are mined based on fuzzy matching of topic words. A large number of experiments show that the proposed framework significantly improves the performance of topic detection and outperforms the selected baseline methods.

Author

M.Vengateshwaran,K. Swarnamalya