Predictive Analysis of Resource Usage Data in Academic Libraries using the VADER Sentiment Algorithm
Gouri Shashank Deo, Ayushi Mishra, Zuber Mohammed Jalaluddin, Chaitanya Vijaykumar Mahamuni · 2020
In any educational campus, a well-equipped library is the most essential requirement, as it is a complete storehouse of information - comprising books, magazines, articles, research papers, and other important documents; not only in print but also on digital media. The most prominent change observed in the library's design of educational institutions in recent days is automation. Library automation allows the librarians easy cataloging of books and maintaining proper records. In big institutions, a pool of resource usage data is generated. This data can be productively used to understand the learning approach of students by professors. Hence, the purpose is to apply sentiment analysis on it for predicting the use of books and resources that would help the qualitative up-gradation of the library. This paper presents the analysis of the data for the renewal of books and resources in the existing work. The progress of the work to date is discussed in the paper.