This project implements a Dining Concierge Chatbot that recommends restaurants based on user preferences, leveraging Flask, MongoDB, and OpenAI's GPT-4 model.
Ensure to replace the API keys and database credentials with your values in the configuration file or environment variables.
The primary goal of the Hotel Sentiment Analysis project is to equip hotel managers with actionable insights into customer feedback, enabling them to enhance service quality and boost customer satisfaction. By automating sentiment analysis, the project aims to streamline the review management process and provide data-driven recommendations.
A significant challenge was ensuring the accuracy of sentiment analysis while preserving the hotel's brand voice in automated responses. Additionally, integrating the system into existing workflows without disrupting operations was a key consideration.
To develop an automated review management system that enables managers to respond to client feedback efficiently with tailored responses.
The project successfully reduced the time required for review management by providing prompt and consistent responses. This improvement in efficiency allowed hotel managers to focus on other critical aspects of hospitality, ultimately leading to enhanced customer satisfaction and operational effectiveness.
