Speech-to-text and sentiment analysis for a hotel feedback system
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...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2023
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| Online Access: | http://eprints.utar.edu.my/6015/ http://eprints.utar.edu.my/6015/1/fyp_IB_2023_LJJ.pdf |
| Summary: | 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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