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dc.contributor.authorSpatiotis, Nikolaos
dc.contributor.authorPerikos, Isidoros
dc.contributor.authorMporas, Iosif
dc.contributor.authorParaskevas, Michael
dc.date.accessioned2020-04-03T00:07:01Z
dc.date.available2020-04-03T00:07:01Z
dc.date.issued2020-03-31
dc.identifier.citationSpatiotis , N , Perikos , I , Mporas , I & Paraskevas , M 2020 , ' Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments ' , International Journal on Artificial Intelligence Tools , vol. 29 , no. 2 , 2040004 . https://doi.org/10.1142/S0218213020400047
dc.identifier.issn0218-2130
dc.identifier.urihttp://hdl.handle.net/2299/22545
dc.description© 2020 World Scientific Publishing Company. Electronic version of an article published as International Journal on Artificial Intelligence Tools, Vol. 29, No. 02, 2040004 (2020): https://doi.org/10.1142/S0218213020400047.
dc.description.abstractLearners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.en
dc.format.extent28
dc.format.extent773919
dc.language.isoeng
dc.relation.ispartofInternational Journal on Artificial Intelligence Tools
dc.subjectText mining
dc.subjectdata mining
dc.subjectfeature extraction
dc.subjectfeature selection
dc.subjectmachine learning
dc.subjectopinion mining
dc.subjectArtificial Intelligence
dc.titleSentiment Analysis of Teachers Using Social Information in Educational Platform Environmentsen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionBioEngineering
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.date.embargoedUntil2021-03-31
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85083033195&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1142/S0218213020400047
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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