%0 Generic %A Wayne, Xin ZHAO %A JIANG, Jing %A WENG, Jianshu %A HE, Jing %A LIM, Ee Peng %A YAN, Hongfei %A LI, Xiaoming %D 2011 %T Twitter-LDA %U https://researchdata.smu.edu.sg/articles/dataset/Twitter-LDA/12062730 %R 10.25440/smu.12062730.v1 %2 https://github.com/minghui/Twitter-LDA %K text mining %K tweet analysis %K LDA %K twitter %K Pattern Recognition and Data Mining %X

Latent Dirichlet Allocation (LDA) has been widely used in textual analysis. The original LDA is used to find hidden "topics" in the documents, where a topic is a subject like "arts" or "education" that is discussed in the documents. The original setting in LDA, where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets. As experiments in [7] have shown that T-LDA could capture more meaningful topics than LDA in Microblogs.

The original setting in Latent Dirichlet Allocation (LDA), where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets.

Related Publication: Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. In Advances in Information Retrieval (pp. 338-349). http://doi.org/10.1007/978-3-642-20161-5_34

%I SMU Research Data Repository (RDR)