CS-BTM: a semantics-based hot topic detection method for social network

Niu, Weinan; Tan, Wenjun; Wei, Jia

doi:10.1007/s10489-022-03500-9

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…The BTM, by observing the co-occurrence patterns of word pairs across the entire dataset, can effectively provide a better model for short texts to infer topics. The BTM is also widely used in text clustering [18], topic mining [19], and hot topic detection [20], among others. In order to achieve better results, scholars have improved the BTM.…”

Section: Literature Reviewmentioning

confidence: 99%

WES-BTM: A Short Text-Based Topic Clustering Model

Zhang,

Gao,

Jia

2023

Symmetry

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User comments often contain their most practical requirements. Using topic modeling of user comments, it is possible to classify and downscale text data, mine the information in user comments, and understand users’ requirements and preferences. However, user comment texts are usually short and lack rich word frequency and contextual information with sparsity. The traditional topic model cannot model and analyze these short texts well. The biterm topic model (BTM), while solving the sparsity problem, suffers from accuracy and noise problems. In order to eliminate information barriers and further ensure information symmetry, a new topic clustering model, termed the word-embedding similarity-based BTM (WES-BTM), is proposed in this paper. The WES-BTM builds on the BTM by converting word pairs into word vectors and calculating their similarity to perform word pair filtering, which in turn improves clustering accuracy. Based on the experimental results using actual data, the WES-BTM outperforms the BTM, LDA, and NMF models in terms of topic coherence, perplexity, and Jensen–Shannon divergence. It is verified that the WES-BTM can effectively reduce noise and improve the quality of topic clustering. In this way, the information in user comments can be better mined.

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Section: Literature Reviewmentioning

confidence: 99%