Sentimart: A Web-Based Ordering, Inventory & Feedback System using Long Short-Term Memory (LSTM) - Based Sentiment Analysis for RTEA Shop

SENTIMART: A Web-Based Ordering, Inventory, and Feedback System based on Long Short-Term Memory (LSTM)-based Sentiment Analysis on RTEA Shop is a one-stop web-based system that was created to help manage operational drawbacks in the field of specialty tea retail. The main innovation of it is that it applies the LSTM-based sentiment analysis algorithm to customer feedback to produce predictive analysis of the feedback, used to forecast the inventory and customized advice on which products to purchase. Constructed using PHP-MySQL and evaluated as per ISO 25010 standards of Software Quality, the system proved to be very functional, reliable, user friendly and efficient. SENTIMART automates and data-driven characteristics streamline the operations, decrease the number of manual activities, and improve customer experience. According to the reviews of both technical specialists and end customers, the system has a good consensus in ISO 25010 results in assessments, which proves the effectiveness of the system as a trustworthy and convenient tool to enhance the services of RTEA Shop.
Technical respondents expressed confidence in the system's sound architecture and the precision of its LSTM sentiment analysis engine, giving it excellent grades for functionality and reliability. Although it was still given a high rating, portability scored somewhat lower, indicating a small opportunity for future improvement in terms of cross-platform consistency.
This paper Sentimart: A Web-Based Ordering, Inventory & Feedback System using Long Short-Term Memory (LSTM)-Based Sentiment Analysis for RTEA Shop” is a developmental type of technology research that aims to design, develop, and evaluate a web-based system guided by the ISO/IEC 25010 software quality model focusing on functionality, reliability, efficiency, usability, and portability. The development follows the Waterfall Model, which involves sequential phases such as requirements analysis, system design, implementation, testing, deployment, and maintenance to ensure systematic progress and quality assurance. Data collection instruments include a needs assessment survey and interview conducted before development to identify user requirements, and a Likert-scale questionnaire administered after system implementation to evaluate user satisfaction and system performance based on ISO 25010 criteria. Each phase ensures that the system meets expected behavioral standards and technical quality attributes for optimal performance. Overall, this structured approach guarantees that Sentimart delivers a reliable, efficient, and user-friendly solution adaptable to various platforms.
The study concludes that SENTIMART is a reliable, efficient, and easy-to-use solution that can improve specialized tea stores' customer-facing services as well as their operational structure. The researchers suggest additional improvements based on the evaluation results and project constraints. These include including more input channels like speech and emoji analysis, putting OTP verification into place for enhanced security, and refining the server infrastructure and LSTM model to get rid of any possible real-time processing delays under heavy traffic. Furthermore, it is recommended to conduct a continuous trial to more accurately evaluate the ystem's long-term impact on sales and customer loyalty, ensuring its scalability and sustaining success in the competitive food and beverage industry.

SENTIMART, LSTM, Sentiment Analysis

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