RESTAURANT REVIEW ANALYSIS USING NLP

This paper presents an advanced Natural Language Processing (NLP) framework for analyzing restaurant reviews to extract valuable insights about customer satisfaction, culinary trends, and operational efficiency. By leveraging state-of-the-art NLP techniques, including sentiment analysis, topic modeling, and named entity recognition (NER), our framework systematically processes large volumes of unstructured textual data sourced from online review platforms. We employ sentiment analysis to gauge customer sentiment at a granular level, identifying key areas of positive and negative feedback. Topic modeling is utilized to uncover prevalent themes and topics within the reviews, providing a comprehensive overview of common customer concerns and preferences. Additionally, NER is applied to pinpoint specific entities such as dish names, ingredients, and service aspects, facilitating a detailed breakdown of elements influencing customer experiences. Our framework’s efficacy is validated through a case study on a diverse dataset of restaurant reviews. Results demonstrate its capability to accurately reflect customer sentiments, detect emerging trends, and highlight critical operational aspects, thereby offering actionable insights for restaurant management and stakeholders. The findings underscore the potential of NLP in transforming qualitative customer feedback into quantitative data that can drive strategic decisionmaking in the restaurant industry. This approach not only enhances our understanding of customer preferences but also provides a robust tool for continuous improvement in service quality and culinary offerings.