Sparse Biterm Topic Model for Short Texts

• Published in: Web and Big Data pp. 227 - 241
• Main Authors: Zhu, Bingshan, Cai, Yi, Zhang, Huakui
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Short texts; Topic modeling; Topic sparsity
• ISBN: 9783030858957; 3030858952
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-85896-4_19

Extracting meaningful and coherent topics from short texts is an important task for many real world applications. Biterm topic model (BTM) is a popular topic model for short texts by explicitly model word co-occurrence patterns in the corpus level. However, BTM ignores the fact that a topic is usually described by a few words in a given corpus. In other words, the topic word distribution in topic model should be highly sparse. Understanding the sparsity in topic word distribution may get more coherent topics and improve the performance of BTM. In this paper, we propose a sparse biterm topic model (SparseBTM) which combines a spike and slab prior into BTM to explicitly model the topic sparsity. Experiments on two short texts datasets show that our model can get comparable topic coherent scores and higher classification and clustering performance than BTM.