In this study, sentiment analysis, commonly referred to as opinion mining or emotional artificial intelligence (AI), is used to obtain student comments about institutional facilities. To ascertain whether an online text has a good, negative, or neutral emotional tone, it must first be analyzed. Natural Language Processing (NLP) includes the subfield of sentiment analysis, and NLP can be used to categorize and extract information with the aid of machine learning methods. Finding out if students are content with the amenities or services provided is important since, in an educational setting, they are consumers. This study of the resources and services that the libraries offer evaluated college students' perceptions of the books, audio CDs, and video CDs, the services provided by the library staff, and the personal computers that are made available to them as part of their facilities. In the current study, surveys were carried out to gather information and assess how well the needs of the students are addressed. On Kaggle.com, secondary data from a North Indian institution was used in the experiments. The research approach was sentiment analysis using a machine learning framework. The F1-score, a harmonic mean of precision and recall based on the attitudes evaluated by the algorithms, was calculated using a text-based classification method using Naive Bayes Multinomial and Support Vector machine learning algorithms. The findings offer views on how successfully library facilities are managed in the form of student sentiments.
