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Rome's Libraries Readers' Comments Analysis with Deep Learning


This posts describes, along with Python code, an analysis of the readers' comments open dataset from Rome's libraries made publicly available by "Istituzione Biblioteche di Roma"¹. The analysis leverages topic modeling techniques to find recurring topics among readers' comments, and thus determine, by inference, the themes of the borrowed books and the interests of the readers. Moreover, sentiment analysis is performed to determine whether customers comments are positive or negative. Finally, readers data (age and occupation) are used to achieve customers segmentation via clustering techniques. This provides insights on the topics of borrowed books, the readers sentiment and different readers clusters.