Voice feedback system with sentiment analysis at a University
This project entails the development of an Android mobile application that leverages Natural Language Processing (NLP), Sentiment Analysis, and integrated voice recognition technology. The primary objective of this application is to address the challenge of inefficient feedback collection and ana...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2023
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| Online Access: | http://eprints.utar.edu.my/6010/1/fyp_IB_2023_CJJ.pdf http://eprints.utar.edu.my/6010/ |
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| Summary: | This project entails the development of an Android mobile application that leverages Natural
Language Processing (NLP), Sentiment Analysis, and integrated voice recognition
technology. The primary objective of this application is to address the challenge of inefficient
feedback collection and analysis methods, which often involve lengthy surveys, limited
feedback scope, and difficulties in handling substantial data volumes.
The proposed application introduces an enhanced approach to submitting, gathering, and
analyzing feedback. It empowers users to submit feedback through voice recognition
technology, which is subsequently converted into text and subjected to sentiment analysis
techniques. The feedback is then categorized based on polarity (ranging from slightly positive
to slightly negative, positive, negative, or neutral) and emotion (such as anger, happiness,
sadness, disappointment, fear, etc.).
Within the application, users are granted access to a history of their previous feedback
submissions. This feature not only allows them to review the feedback content but also
provides insights into the sentiment analysis results for each entry. Furthermore, users retain
the option to selectively remove specific feedback entries, ensuring transparency and granting
them control over their submitted feedback.
Additionally, the application includes administrative functionalities for management or
system administrators. These features enable them to organize and categorize the collected
feedback in an organized manner, primarily based on the sentiments expressed. Furthermore,
categorization is facilitated according to the type of user who submitted the feedback, aiding
management in customizing responses and actions based on specific user groups and their
feedback. To mitigate the impact of potentially malicious anonymous feedback, the system
allows management to reduce the weightage of such submissions.
In summary, this application offers a streamlined approach to feedback collection and
analysis, enhancing the user experience while empowering organizations to make data-driven
decisions for future enhancements. |
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