Online Consumer Sentiment Analysis for Product Quality Assessment
Summary
Online consumer sentiment analysis for product quality assessment combines natural language processing and machine learning techniques to extract evaluative information from user‐generated content such as reviews, forums and social media posts. By quantifying and characterising opinions on functionality, durability, safety and aesthetics, this approach enables manufacturers and regulators to monitor product performance in real time, identify emerging defects, and prioritise corrective actions. Common methodologies include lexicon‐based sentiment scoring, supervised classification, topic modelling and aspect‐based sentiment analysis, which together provide both high‐level satisfaction metrics and fine‐grained insights into specific attributes. Advances in deep learning have further enhanced the ability to interpret nuanced language, irony and mixed sentiments, while multimodal analysis incorporating images and metadata has broadened the scope of quality indicators. Globally, this analytical paradigm supports more responsive supply‐chain management, evidence‐based consumer protection policies and data‐driven product design, with measurable benefits such as reduced recall rates and improved customer loyalty.
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Online Consumer Sentiment Analysis for Product Quality Assessment publication trend
The graph below shows the total number of articles in online consumer sentiment analysis for product quality assessment across all publications each year (not limited to Nature Index journals).
Technical terms
Sentiment analysis: Computational process of determining the emotional tone behind text to assess positive, negative or neutral opinions.
Aspect‐based sentiment analysis: Fine‐grained technique that associates sentiments with specific product features or attributes.
Topic modelling: Unsupervised method to discover latent thematic structures within a large corpus of documents.
Risk assessment model: Framework for quantifying hazard likelihood and severity based on textual indicators in consumer feedback.
Content analysis: Systematic coding and interpretation of text data to identify patterns and trends in user‐generated content.
References
- Safety Concerns in Mobility-Assistive Products for Older Adults: Content Analysis of Online Reviews. Journal of Medical Internet Research (2023).
- A semiautomated risk assessment method for consumer products. Risk Analysis (2023).
- Research on the Perceived Quality of Virtual Reality Headsets in Human–Computer Interaction. Sensors (2023).
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