The Hotel Sentiment Analysis Dashboard is a Flask-based web application that offers a dynamic overview of customer sentiments across various hotel services such as the pool, breakfast, room, service, and staff. It utilizes Natural Language Processing (NLP) to analyze customer feedback and visualize sentiment trends, helping hotel management to identify areas of improvement and maintain high standards of customer satisfaction.
The application integrates several Python libraries to perform its tasks:
The application processes input data from an Excel file containing customer feedback, analyzes the sentiment for categorized keywords (like 'pool', 'room', etc.), and then visualizes these sentiments over time. It dynamically generates recommendations based on the analysis, which are displayed on the dashboard.
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.