Earticle

In recent years, topic detection has become a hot research point of the social network, which can be very good to find the key factors from the massive information and thus discover the topics. The traditional label propagation-based topic discovery algorithm (LPA) is widely concerned because of its approximate linear time complexity and there is no need to define the target function. However, LPA algorithm has the uncertainty and the randomness, which affects the accuracy and the stability of the topic discovery. In this paper, a method for clustering label words based on mutual information analysis is presented to find the current topic. Firstly, through filtering the stop words and extracting keywords with TF-IDF, topic words are been extracted out, and then a common word matrix is built, a topic discovery algorithm based on mutual information and label clustering is put forward. Finally, extensive experiments on two real datasets validate the effectiveness of the proposed MI-LC (Mutual information-Label clustering) algorithm against other well-established methods LPA and LDA in terms of running time, NMI value and perplexity value.

Twitter has turned out to be one of the biggest microblogging stages for clients around the globe to impart anything happening around them to companions and past. A bursty theme in Twitter is one that triggers a surge of applicable tweets inside a brief timeframe, which frequently reflects vital occasions of mass intrigue. Step by step instructions to influence Twitter for early location of bursty points has in this way turn into a vital research issue with enormous viable esteem. Regardless of the abundance of research work on subject demonstrating and investigation in Twitter, it remains a test to distinguish bursty themes progressively. As existing strategies can barely scale to handle the assignment with the tweet stream progressively, we propose in this paper TopicSketch, a draw based subject model together with an arrangement of procedures to accomplish continuous discovery. We assess our answer on a tweet stream with more than 30 million tweets. Our investigation comes about show both productivity and viability of our approach. Particularly it is additionally exhibited that TopicSketch on a solitary machine can possibly handle several millions tweets for each day, which is on the same scale of the total number of daily tweets in Twitter, and present bursty events in finer-granularity.