Voice feedback system with sentiment analysis at a University - Chan, Jun Jie (2023)
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.