Cross-Domain Polarity Models to Evaluate User eXperience in E-learning
| Autor: | Lluís F. Hurtado, Maria Jose Castro-Bleda, Rosario Sanchis-Font, José-Ángel González, Ferran Pla |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer Networks and Communications Computer science 02 engineering and technology Semantic domain Sentiment analysis 020901 industrial engineering & automation User experience design Artificial Intelligence Human–computer interaction Machine learning 0202 electrical engineering electronic engineering information engineering Virtual learning environments Artificial neural network Artificial neural networks User experience business.industry General Neuroscience MeaningCloud User experience evaluation Virtual learning environment 020201 artificial intelligence & image processing Learning Management Learning management systems business LENGUAJES Y SISTEMAS INFORMATICOS Software |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| Popis: | [EN] Virtual learning environments are growing in importance as fast as e-learning is becoming highly demanded by universities and students all over the world. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus with the opinions given by a total of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses has been built. All the collected opinions were manually labeled with polarity information (positive, negative or neutral) by three human annotators, both at the whole opinion and sentence levels. We have applied our state-of-the-art sentiment analysis models, trained with a corpus of a different semantic domain (a Twitter corpus), to study the use of cross-domain models for this task. Cross-domain models based on deep neural networks (convolutional neural networks, transformer encoders and attentional BLSTM models) have been tested. In order to contrast our results, three commercial systems for the same task (MeaningCloud, Microsoft Text Analytics and Google Cloud) were also tested. The obtained results are very promising and they give an insight to keep going the research of applying sentiment analysis tools on User eXperience evaluation. This is a pioneering idea to provide a better and accurate understanding on human needs in the interaction with virtual learning environments and a step towards the development of automatic tools that capture the feed-back of user perception for designing virtual learning environments centered in user's emotions, beliefs, preferences, perceptions, responses, behaviors and accomplishments that occur before, during and after the interaction. Partially supported by the Spanish MINECO and FEDER founds under Project TIN2017-85854-C4-2-R. Work of J.A. Gonzalez is financed under Grant PAID-01-17 |
| Databáze: | OpenAIRE |
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