Twitter-LDA

• Main Authors: ZHAO, Wayne Xin, JIANG, Jing, WENG, Jianshu, HE, Jing, LIM, Ee Peng, YAN, Hongfei, LI, Xiaoming
• Format: text
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/researchdata/12; https://github.com/minghui/Twitter-LDA
• Institution: Singapore Management University

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.