Sentiment Analysis in IoT Data Streams: An NLP-Based Strategy for Understanding Customer Responses
Keywords:
Sentiment Analysis, IoT Data Streams, Natural Language Processing (NLP), Customer Response, Edge Computing, Multi-Modal Data, Real-Time Sentiment DetectionAbstract
This research uses NLP to analyze IoT data streams for sentiment analysis to understand and react to consumer emotions and actions in real-time. This study investigates how NLP can handle multi-modal IoT data, including text, speech, and sensor measurements, to discover sentiment indicators and deliver customer satisfaction insights. The research addresses the problems of incorporating real-time sentiment analysis into IoT contexts via a secondary data assessment, including data volume, velocity, and multi-modal model computational complexity. The key results include multi-modal data integration, real-time processing frameworks, and edge computing for sentiment analysis. Contextual sensitivity and model improvement methods like distillation also improve sentiment accuracy. The paper also emphasizes explainability in AI models, particularly in sensitive applications, and recommends clear, ethical frameworks to protect data privacy and user trust. Policy implications show that IoT settings require strong data privacy and AI transparency policies to protect consumer data and promote ethical usage of AI-driven sentiment analysis technology. The study indicates that NLP-based sentiment analysis may improve IoT customer experience by providing real-time, data-driven insights into user preferences and behaviors.
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