Natural Language Processing and Sentiment Analysis for Support Desk Comment Review


This project mainly reflects the importance of mining the Support Desk User Survey Comments and also analyzing the impact these comments create on the Integrated Logistics Management System (ILMS). Although sentiment analysis was used in this project, it is not limited to it. Many other methods in AWS can be implemented for analyzing and understanding user’s survey comments. Through this project, the ILMS benefits from the fact that instead of going through a lot of comments in order to know about the users’ overall satisfaction with the Support Desk, this analysis helps ILMS to directly view the percentage of positive, negative, and neutral reviews and the relevant frequent words in each comment, and also drives to help ILMS to get an idea of the product in less time and to take further decisions on how and in what areas to improve.

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Future Work 

The biggest problem of this project is transparency. Since the data is confidential, we can only perform tasks on the prescribed platform as AWS environment. As future enhancements, the team would also like to expand more tools available for analysis and dashboard creation, such as combine Tableau. To stay competitive, companies should be consider allocating more resources to facilitate user access to the appropriate systems. Additionally, customers don’t like waiting for a solution to their problems, which means companies should be committed to transforming themselves into more advanced self-service options, such as the use of chatbots may be utilized. 

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