Research Area:  Machine Learning

Abstract:

Despite the extensive application of topic models in natural language processing tasks in recent years, the Chinese texts of short comments characterised by large scale, high noise and small information points have put forward higher requirements for the accuracy and stability of the results, which fails to be satisfied by existing topic models. In this paper, a product typicality attribute mining method based on a topic clustering ensemble was proposed. By introducing multiple topic models into ensemble learning, the problems of semantic representation loss, clustering inefficiency and lack of interpretability in the mining of product typicality attributes of short comment texts should be solved. By an effective combination of the topic clustering algorithm based on the diversity of speech, the topic clustering ensemble algorithm based on the Non-negative matrix factorization, and the interpretation method of product typicality attributes based on the mean-shift algorithm, an unsupervised model of product typicality attribute mining for short comment texts is constructed. As shown by the experimental results, the modelling method assumes favourable performance in topic clustering and feature selection, suggesting its advantages in product typicality attribute identification and interpretability compared with common methods.

Keywords:  
LDA models
Integrated learning
Non-negative matrix factorization
Mean-shift algorithm

Author(s) Name:  Jing-Tao Sun,Qiu-Yu Zhang

Publisher name:  Springer

DOI:  10.1007/s10462-022-10163-y

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s10462-022-10163-y