PRODUCT REVIEW EMOTION ANALYZER USING BILSTM WITH FACTOR MAPPING AND MATPLOTLIB VISUALIZATION

Customer opinion analysis is paramount for design of products and user experience enhancement. The present paper proposes a Bidirectional Long Short-Term Memory (BILSTM)-driven method for customer review analysis based on extracting happiness, sadness and neutral opinions. Unlike general sentiment analysis, users manually specify the product attributes like aesthetics, ergonomics or usability, which leads to enhanced emotion-to design mapping .Trained on a labelled dataset, the BILSTM model achieves more than 95% accurate emotion classification by capturing complex linguistic patterns. An interactive Matplotlib based plot facilitates result interpretation, allowing users to examine sentiment trends and draw actionable conclusions. This system enables a data-driven product design by linking emotional feedback with particular attributes, closing the loop between sentiment analysis and product development. KEYWORDS Emotion Recognition, BILSTM, Emotion Analysis, Product Design, Customer Feedback, Matplotlib Visualization ,Product Development