BTM: Topic Modeling over Short Texts

By Xueqi Cheng, Xiaohui Yan, Yanyan Lan, Jiafeng Guo. IEEE Transactions on Knowledge and Data Engineering, 2014

532 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, methodology): “Following [8], we use the PMI-Score proposed in [23] to calculate topic coherence.”
Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis (2020, background): “Various TM methods can automatically extract topics from short texts (Cheng et al., 2014) and standard long-text data (Xie and Xing, 2013).”
Topic Modeling Using Latent Dirichlet allocation (2021, methodology): “To speed up the sampling process for the biterm itself, researchers have offered the extended version known as FastBTM [45, 171].”
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “m generates discriminative topic representations as well as rather coherent topics in short texts. The main advantage of BTM prevents the data sparsity issue with learning a global topic distribution [96, 97]. TOT-MMM, introduced a hashtag recommendation that called TOT-MMM, This approach is a hybri”
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, methodology): “STTM provides implementations of DMM [18], LF-DMM [28], GPU-DMM [29], GPU-PDMM [31], BTM [14], WNTM [32], SATM [20], and PTM [35].”