ArticlePeerJ. Computer science2024
Unlocking insights: integrated text mining and interpretive structural modeling for enhanced user review analysis.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Effective keywords are extracted from the massive milk product user review data to construct thematic terms and explore the elemental influence relationships to assist manufacturers, and e-commerce platforms in understanding user behaviour and preferences and further optimise product design and marketing strategies. By fusing two different text mining methods, term frequency-inverse document frequency (TF-IDF) and Word2vec, we explore the semantic relationships, then visualise the relevance of user reviews by drawing knowledge graphs with Neo4j, and finally, be able to explore the relationship between the themes of the mined reviews, interpretative structural model (ISM) was used for a comprehensive evaluation, and the effectiveness of the method was verified on the Suning.com website dataset. The fusion of text mining and systematic analysis helps users to locate products quickly and precisely from the huge review information. The six elements of user reviews were categorized as freshness of taste, discounted prices, logistics, customer repurchase, product packaging, nutritional composition, and their element levels were divided into three layers. the first layer was discounted prices, customer repurchase, and logistics; the second layer was product packaging and nutritional composition; and the third layer was taste freshness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.