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  4. Vol 7, Issue 1, 2026

Density-Based Clustering and Latent Dirichlet Allocation Framework for Consumer Preference Mining in E-Commerce Reviews

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DOI: 10.23977/autml.2026.070120 | Downloads: 2 | Views: 99

Author(s)

Weiyi Zhu 1, Ming Yin 1, Yan Zhang 1, Yanan Peng 1

Affiliation(s)

1 School of Big Data and Statistics, Sichuan Tourism University, Chengdu, Sichuan, China

Corresponding Author

Yanan Peng

ABSTRACT

Consumer preference inference from large-scale electronic commerce reviews remains a fundamentally challenging task due to the high dimensionality, sparsity, and noise characteristics inherent in user-generated textual content. This paper presents an integrated text mining framework that combines density-based spatial clustering with probabilistic topic modeling to extract structured preference signals from unstructured online review corpora. The proposed architecture employs the DBSCAN algorithm to partition product entries into coherent price segments without requiring prior specification of cluster count, applies a Jieba-based tokenization pipeline with custom stopword filtering for Chinese text normalization, and trains a Latent Dirichlet Allocation model whose optimal topic count is selected via inter-topic cosine similarity minimization. A web crawler built on Requests and BeautifulSoup collected 9,234 consumer reviews together with associated product metadata, which were partitioned into twelve density-coherent price clusters revealing two dominant preference intervals near 56 and 70 currency units. The LDA model identified three latent topics in positive reviews and two in negative reviews, achieving a perplexity of 287.4 and a topic coherence of 0.524, representing an 18.7% improvement over comparable LSA and NMF baselines. Sentiment-aware classification reached 92.6% accuracy with an F1 score of 91.0%, providing actionable insights for product design optimization and personalized recommendation in electronic commerce platforms.

KEYWORDS

Latent Dirichlet Allocation, Density-Based Spatial Clustering, Chinese Text Tokenization, Consumer Preference Mining, Probabilistic Topic Modeling, E-Commerce Review Analysis

CITE THIS PAPER

Weiyi Zhu, Ming Yin, Yan Zhang, Yanan Peng. Density-Based Clustering and Latent Dirichlet Allocation Framework for Consumer Preference Mining in E-Commerce Reviews. Automation and Machine Learning (2026). Vol. 7, No. 1, 162-172. DOI: http://dx.doi.org/10.23977/autml.2026.070120.

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