Speech-to-text and sentiment analysis for a hotel feedback system

Lee, Jia Jet (2023) Speech-to-text and sentiment analysis for a hotel feedback system. Final Year Project, UTAR.

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Abstract

Natural language processing (NLP) refers to a set of AI techniques that enable computers to understand human communication in the form of text or speech. NLP leverages computational linguistics and rule-based modeling in conjunction with statistical, machine learning, and deep learning models to effectively handle human language. By utilizing these technologies, computers are capable of comprehending the meaning and intention behind human language, as well as identifying the sentiment expressed. One key application of NLP is multiclass text categorization, which allows computers to classify text as positive or negative. The proposed project aims to develop a mobile application for a hotel feedback system with speech-to-text and sentiment analysis features. The speech input from customers will be converted into text and then subjected to a classification task using machine learning to identify positive and negative feedback. By analyzing the feedback data, the hotel management can determine the rate of customer feedback and make improvements accordingly. Once the hotel management obtains information on the rate of customer feedback, they will be encouraged to enhance the hotel's value and prevent negative feedback from customers. Additionally, in the event of negative feedback, the hotel management will take appropriate action to address the issues and prevent them from recurring. This feedback system will enable the hotel to make informed decisions and improve its performance.

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