Sentiment analysis through twitter as a mechanism for assessing university satisfaction
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Authors
Chamorro-Atalaya, OmarArce-Santillan, Dora
Morales-Romero, Guillermo
Ramos-Salazar, Primitiva
León-Velarde, César
Auqui-Ramos, Elizabeth
Levano-Stella, Miguel
Issue Date
2022-10-01
Metadata
Show full item recordJournal
Indonesian Journal of Electrical Engineering and Computer ScienceDOI
10.11591/ijeecs.v28.i1.pp430-440Additional Links
https://ijeecs.iaescore.com/index.php/IJEECS/article/view/28064Abstract
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.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 International
Language
spaISSN
25024752EISSN
25024760ae974a485f413a2113503eed53cd6c53
10.11591/ijeecs.v28.i1.pp430-440
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- Creative Commons