Abstract
The digitization of business processes and the extraction of answers to user requests for big data are modern problems that are of great interest to scientists and researchers. The data generated so far, located in various corpora, is much more than can be analyzed. Therefore, they are collected, identified, cleaned and normalized to be used most adequately. Segmentation, assumptions and hypotheses contribute to the degree of satisfaction with the returned result. The research proposed a general method for collecting, cleaning and normalizing data from various sources, structurally modelling it into appropriate models, then testing hypotheses and analyzing the obtained results to conclude large academic data that will benefit the business in making management decisions. This is possible with the means of computational linguistics and with the help of Python data manipulation libraries.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
