A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to topic detection and tracking
Topic detection and tracking
Temporal event clustering for digital photo collections
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Detecting topic labels for tweets by matching features from pseudo-relevance feedback
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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How to organize and visualize big amount of text messages stored on one's mobile phone is a challenging problem, since they can hardly be organized by threads as we do for emails due to lack of necessary metadata such as "subject" and "reply-to". In this paper, we propose an innovative approach based on clustering algorithms and natural language processing methods. We first cluster the text messages into candidate conversations based on their temporal attributes, and then do further analysis using a semantic model based on Latent Dirichlet Allocation (LDA). Considering that the text messages are usually short and sparse, we trained the model using a large scale external data collected from twitter-like web sites, and applied the model to text messages. In the end, the text messages are organized as conversations based on their topics. We evaluated our approach based on 122,359 text messages collected from 50 university students during 6 months.