Sentiment analysis through twitter as a mechanism for assessing university satisfaction - Omar Chamorro-Atalaya; Dora Arce-Santillan; Guillermo Morales-Romero; César León-Velarde; Primitiva Ramos-Salaza; Elizabeth Auqui-Ramos; Miguel Levano-Stella (Institute of Advanced Engineering and Science, 2022)
Currently, the data generated in the university environment related to the perception of satisfaction is generated through surveys with categorical response questions defined on a Likert scale, with factors already defined to be evaluated, applied once per academic semester, which generates very biased information. This leads us to wonder why this survey is applied only once and why it only asks about some factors. The objective of the article is to demonstrate the feasibility of a proposal to determine the degree of perception of student satisfaction through the use of data science and natural language processing (NLP), supported by the social network twitter, as an element of data collection. As a result of the application of this proposal based on data science, it was possible to determine the level of student satisfaction, being 57.27%, through sentiment analysis using the Python library