ArticleScientific reports2026
Intelligent classification and dynamic evolution simulation study on air conditioner product demand characteristics.
Article in Scientific reports, 2026. 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
Funding
Abstract
Extracting actionable user insights from online reviews is often compromised by high data noise and static analysis limitations. To address this, this study proposes an integrated framework coupling adaptive noise filtration with multi-dimensional dynamic simulation. Specifically, an Improved Whale Optimization Algorithm (IWOA) tunes Support Vector Machine (SVM) hyperparameters to ensure high-precision review classification. Subsequently, BERTopic extracts demand topics, and temporal cosine similarity tracks their evolution within a dynamic Importance–Performance Analysis (IPA) framework. Simulation reveals that air-conditioner user demands exhibit distinct seasonal nonlinear fluctuations. This quantifiable tool supports demand forecasting and strategic product iteration in the home-appliance industry.
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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.