Research and Analysis of Market Trends with Sentimental Analysis Using SBERT

This Modern sentiment analysis has evolved beyond simple positive/negative classifications to examine specific aspects of customer feedback in detail. This advanced approach breaks down reviews into distinct components like product quality, pricing, and customer service to generate nuanced insights. The implementation moves away from conventional embedding techniques, adopting the more sophisticated SBERT (Sentence-BERT) architecture to process customer comments. SBERT's advanced sentence embedding capabilities offer a marked improvement over traditional methods like Word2Vec and GloVe. By capturing complex semantic relationships and contextual nuances, SBERT enables more refined text analysis. When combined with Support Vector Machine (SVM) classification, this approach effectively categorizes sentiment across three levels: positive, negative, and neutral. The system's strength lies in its ability to detect subtle variations in customer opinions about specific product features. This granularity allows businesses to monitor sentiment patterns across different aspects of their offerings, leading to more targeted improvements. The enhanced contextual understanding provided by SBERT results in more accurate sentiment aggregation and analysis. This sophisticated approach to feedback analysis empowers organizations to make evidence-based decisions more effectively. By providing detailed insights into customer perceptions of specific product elements, companies can quickly identify areas requiring attention and adapt to changing market preferences. The result is a more responsive business strategy that better aligns with customer needs and expectations, ultimately leading to improved product development and customer satisfaction.