Sentiment analysis through twitter as a mechanism for assessing university satisfaction

• Autores: León Velarde, César Gerardo, Chamorro-Atalaya, Omar, Arce-Santillan, Dora, Morales-Romero, Guillermo, Ramos-Salazar, Primitiva, Auqui-Ramos, Elizabeth, Levano-Stella, Miguel
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/6009
• Enlace del recurso: https://hdl.handle.net/20.500.12867/6009; http://doi.org/10.11591/ijeecs.v28.i1.pp430-440
• Nivel de acceso: acceso abierto
• Materia: Sentiment analysis; Student satisfaction; Teacher performance; Virtual learning; Text mining; https://purl.org/pe-repo/ocde/ford#5.03.01; https://purl.org/pe-repo/ocde/ford#2.02.03

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 "NLTK"; Thus, it was also possible to extract texts linked to the relevant factors of teaching performance to achieve student satisfaction, through the term frequency and inverse document frequency (TF-IDF) approach, these being those linked to the use of tools of simulation in the virtual learning process.