{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:59:51Z","timestamp":1772762391790,"version":"3.50.1"},"reference-count":30,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<p>Twitter is one of the largest sources of real-time information on the Internet and is continuously fed by millions of users around the world. Each of these users publishes text messages with their opinions, concerns, information, or simply their daily happenings. It is a challenge to address the analysis of massive data in the network, just as it is an objective to look for ways to understand everything that data can offer today in terms of knowledge of society and the market. The sector of science communication is still discovering everything that the web 2.0 and social networks can offer to reach all audiences. This article develops a classification model of messages launched on Twitter, on science topics, in Spanish, with machine learning techniques. The training of this type of models requires the creation of a specific corpus in Spanish for the subject of science, which is one of the most laborious tasks. The classifier is able to predict the sentiment of the message in real time on Twitter, with a confidence interval greater than 80%. The results of its evaluation are at 72% accuracy.<\/p>","DOI":"10.4018\/jitr.2020070105","type":"journal-article","created":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T15:54:17Z","timestamp":1595001257000},"page":"80-94","source":"Crossref","is-referenced-by-count":5,"title":["Supervised Sentiment Analysis of Science Topics"],"prefix":"10.4018","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6253-7087","authenticated-orcid":true,"given":"Patricia","family":"S\u00e1nchez-Holgado","sequence":"first","affiliation":[{"name":"University of Salamanca, Salamanca, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2636-2849","authenticated-orcid":true,"given":"Carlos","family":"Arcila-Calder\u00f3n","sequence":"additional","affiliation":[{"name":"University of Salamanca, Salamanca, Spain"}]}],"member":"2432","reference":[{"key":"JITR.2020070105-0","doi-asserted-by":"publisher","DOI":"10.3145\/epi.2016.jul.12"},{"key":"JITR.2020070105-1","doi-asserted-by":"publisher","DOI":"10.3145\/epi.2017.sep.18"},{"key":"JITR.2020070105-2","doi-asserted-by":"publisher","DOI":"10.1038\/nj7538-263a"},{"key":"JITR.2020070105-3","doi-asserted-by":"crossref","unstructured":"Baviera, T. (2016). T\u00e9cnicas para el an\u00e1lisis del sentimiento en Twitter: Aprendizaje Autom\u00e1tico Supervisado y SentiStrength [Techniques for sentiment analysis in Twitter\u202f: Supervised Learning and SentiStrength].","DOI":"10.7203\/rd.v1i3.74"},{"key":"JITR.2020070105-4","unstructured":"Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O\u2019Reilly. Retrieved from https:\/\/pdfs.semanticscholar.org\/3673\/bccde93025e05431a2bcac4e8ff18c9c273a.pdf"},{"key":"JITR.2020070105-5","unstructured":"Bollen, J., Mao, H., & Pepe, A. (2011). Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence. 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T., & Ure\u00f1a-L\u00f3pez, L. A. (2016). Resumen de TASS 2016. In TASS 2016: Workshop on Sentiment Analysis at SEPLN Proceedings (pp. 13\u201321). Academic Press. Retrieved from http:\/\/ceur-ws.org\/Vol-1702\/tass2016_proceedings_v24.pdf"},{"key":"JITR.2020070105-11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2011.07.007"},{"key":"JITR.2020070105-12","author":"J.Hern\u00e1ndez Orallo","year":"2004","journal-title":"Introducci\u00f3n a la Miner\u00eda de Datos"},{"key":"JITR.2020070105-13","first-page":"75","article-title":"ELiRF-UPV en TASS 2015: An\u00e1lisis de Sentimientos en Twitter","author":"L.-F.Hurtado","year":"2015","journal-title":"Workshop on Sentiment Analysis at SEPLN co-located with 31st SEPLN Conference"},{"key":"JITR.2020070105-14","unstructured":"Jarreau, P. B. (2015). All the Science That Is Fit to Blog: An Analysis of Science Blogging Practices. Louisiana State University. 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Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval (Vol. 2). Cornell University Press. Retrieved from http:\/\/www.cs.cornell.edu\/home\/llee\/omsa\/omsa.pdf","DOI":"10.1561\/1500000011"},{"key":"JITR.2020070105-20","doi-asserted-by":"publisher","DOI":"10.3109\/A036860"},{"key":"JITR.2020070105-21","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Rodr\u00edguez, A. V., Gonz\u00e1lez-Pedraz, C., & Alonso Berrocal, J. L. (2018). Twitter como herramienta de comunicaci\u00f3n cient\u00edfica en Espa\u00f1a. Principales agentes y redes de comunicaci\u00f3n. Communication Papers, 7(13), 95\u2013111. Retrieved from https:\/\/dialnet.unirioja.es\/servlet\/articulo?codigo=6442315","DOI":"10.33115\/udg_bib\/cp.v7i13.21986"},{"key":"JITR.2020070105-22","doi-asserted-by":"publisher","DOI":"10.15252\/embr.201438979"},{"key":"JITR.2020070105-23","doi-asserted-by":"publisher","DOI":"10.22323\/2.12030205"},{"key":"JITR.2020070105-24","unstructured":"Raes, K. 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