Classifying Temporal Topics with Similar Patterns on Twitter

Abstract

Twitter is a popular microblogging service that enables the users to send and read short text messages. These messages are becoming source to analyze topic trends and identify relations among temporal topics. In this paper, we propose a method to classify the temporal topics on Twitter as a problem of grouping the similar patterns. To provide a starting point for a classification under the same topics, we identify the content word weighting scheme based on Latent Dirichlet Allocation (LDA). And we formulate how the temporal topics in the time window can be classified like peaky topics, constant topics, and periodic topics. We provide different real case studies which show the validity of the proposed method. Evaluations show that the proposed method is useful as a classifying model in the analysis of the temporal topics

Document Classification Model Using Web Documents for Balancing Training Corpus Size per Category

“…Most digital documents are frequently updated, and writers disseminate information and present their ideas on various topics [2]. Unlike news articles written by well-educated journalists and standardized according to official style guides, most digital documents, such as weblogs and twitter tweets, tend to contain colloquial sentences and slang that misleads the classifier, because anybody can publish anything [3][4][5][6]. Considering this cumbersomeness of document classification, researchers have proposed the following approaches.…”