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Perceptual effects of formant enhancement with the factors of phonetic type, listening conditions and language experience of listeners
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Real Pandemic: Institutional Neglect
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In: Teaching Culturally and Linguistically Diverse International Students in Open or Online Learning Environments: A Research Symposium (2021)
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Supporting International Graduate Students: Lessons from a Fall 2020 Non-Credit Course
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In: Teaching Culturally and Linguistically Diverse International Students in Open or Online Learning Environments: A Research Symposium (2021)
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Empathy Mapping: Bridging cultural and linguistic divides in international online education
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In: Teaching Culturally and Linguistically Diverse International Students in Open or Online Learning Environments: A Research Symposium (2021)
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The development and testing of an online scenario-based learning activity to prepare preservice teachers for teaching placements
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In: Test Series for Scopus Harvesting 2021 (2021)
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On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein
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The Effects of multilingualism and Music Experience on Tone and Vowel Discrimination Ability
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In: Electronic Thesis and Dissertation Repository (2021)
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Learn, Try, Repeat: Experiential Learning in Adult Second Language Acquisition of Spanish in Higher Education
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In: Honors Theses, University of Nebraska-Lincoln (2021)
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Bilinguals' and monolinguals' performance on a non-verbal cognitive control task: how bilingual language experience contributes to cognitive performance by reducing mixing and switching costs
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Cross-Domain Polarity Models to Evaluate User eXperience in E-learning
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Abstract:
[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 ; Sanchis-Font, R.; Castro-Bleda, MJ.; González-Barba, JÁ.; Pla Santamaría, F.; Hurtado Oliver, LF. (2021). Cross-Domain Polarity Models to Evaluate User eXperience in E-learning. Neural Processing Letters. 53:3199-3215. https://doi.org/10.1007/s11063-020-10260-5 ; S ; 3199 ; 3215 ; 53 ; Ba J, Kiros JR, Hinton GE (2016) Layer normalization. arxiv:1607.06450 ; Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, conference track proceedings. arxiv:1409.0473 ; Baziotis C, Pelekis N, Doulkeridis C (2017) Datastories at SemEval-2017 task 4: deep LSTM with attention for message-level and topic-based sentiment analysis. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 747–754 ; Cliche M (2017) BB\_twtr at SemEval-2017 task 4: Twitter sentiment analysis with CNNs and LSTMs. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). 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In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019), CEUR-WS, Bilbao, Spain, CEUR workshop proceedings, pp 550–560 ; Godin F, Vandersmissen B, De Neve W, Van de Walle R (2015) Multimedia lab @ ACL WNUT NER shared task: named entity recognition for Twitter microposts using distributed word representations. In: Proceedings of the workshop on noisy user-generated text. Association for Computational Linguistics, Beijing, China, pp 146–153. https://doi.org/10.18653/v1/W15-4322. https://www.aclweb.org/anthology/W15-4322 ; González J, Pla F, Hurtado L (2018) Elirf-upv en TASS 2018: Análisis de sentimientos en twitter basado en aprendizaje profundo (elirf-upv at TASS 2018: sentiment analysis in Twitter based on deep learning). In: Proceedings of TASS 2018: workshop on semantic analysis at SEPLN, TASS@SEPLN 2018, co-located with 34nd SEPLN conference (SEPLN 2018), Sevilla, Spain, September 18th, 2018, pp 37–44. http://ceur-ws.org/Vol-2172/p2_elirf_tass2018.pdf ; González J, Hurtado L, Pla F (2019) Elirf-upv at TASS 2019: transformer encoders for Twitter sentiment analysis in Spanish. In: Proceedings of the Iberian languages evaluation forum co-located with 35th conference of the Spanish Society for Natural Language Processing, IberLEF@SEPLN 2019, Bilbao, Spain, September 24th, 2019, pp 571–578. http://ceur-ws.org/Vol-2421/TASS_paper_2.pdf ; González JÁ, Pla F, Hurtado LF (2017) ELiRF-UPV at SemEval-2017 task 4: sentiment analysis using deep learning. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 723–727. https://doi.org/10.18653/v1/S17-2121. https://www.aclweb.org/anthology/S17-2121 ; González JÁ, Hurtado LF, Pla F (2019) ELiRF-UPV at TASS 2019: transformer encoders for Twitter sentiment analysis in Spanish. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019), CEUR-WS, Bilbao, Spain, CEUR workshop proceedings ; GoogleCloud (2019) Cloud natural language API. https://cloud.google.com/natural-language/. Accessed 27 Dec 2019 ; Hassenzahl M, Tractinsky N (2006) User experience—a research agenda. Behav Inf Technol 25(2):91–97. https://doi.org/10.1080/01449290500330331 ; Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 ; Hurtado Oliver LF, Pla F, González Barba J (2017) ELiRF-UPV at TASS 2017: sentiment analysis in Twitter based on deep learning. In: TASS 2017: workshop on semantic analysis at SEPLN, pp 29–34 ; IBM (2019) Natural language understanding. https://www.ibm.com/watson/services/natural-language-understanding/. Accessed 27 Dec 2019 ; ISO 9241-210:2019 (2019) Ergonomics of human-system interaction—part 210: human-centred design for interactive systems. International Standardization Organization (ISO). https://www.iso.org/standard/77520.html. Accessed 27 Dec 2019 ; Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP 2014, October 25–29, 2014, Doha, Qatar, a meeting of SIGDAT, a special interest group of the ACL, pp 1746–1751. http://aclweb.org/anthology/D/D14/D14-1181.pdf ; Krippendorff K (2004) Reliability in content analysis. Hum Commun Res 30(3):411–433 ; Kujala S, Roto V, Väänänen-Vainio-Mattila K, Karapanos E, Sinnelä A (2011) UX curve: a method for evaluating long-term user experience. Interact Comput 23(5):473–483 ; Liu B (2012) Sentiment analysis and opinion mining. A comprehensive introduction and survey. Morgan & Claypool Publishers, San Rafael ; Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on world wide web. ACM, New York, NY, USA, WWW ’05, pp 342–351. https://doi.org/10.1145/1060745.1060797 ; Luque FM (2019) Atalaya at TASS 2019: data augmentation and robust embeddings for sentiment analysis. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019), CEUR-WS, Bilbao, Spain, CEUR workshop proceedings ; Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Association for computational linguistics (ACL) system demonstrations, pp 55–60. http://www.aclweb.org/anthology/P/P14/P14-5010 ; Martínez-Cámara E, Díaz-Galiano M, García-Cumbreras M, García-Vega M, Villena-Román J (2017) Overview of TASS 2017. In: Proceedings of TASS 2017: workshop on semantic analysis at SEPLN (TASS 2017), CEUR-WS, Murcia, Spain, CEUR workshop proceedings, vol 1896 ; MeaningCloud (2019) Demo de Analítica de Textos. https://www.meaningcloud.com/es/demos/demo-analitica-textos. Accessed 27 Dec 2019 ; MeaningCloud (2019) MeaningCloud: Servicios web de analítica y minería de textos. https://www.meaningcloud.com/. Accessed 27 Dec 2019 ; MicrosoftAzure (2019) Text analytics API. https://azure.microsoft.com/es-es/services/cognitive-services/text-analytics/. Accessed 27 Dec 2019 ; Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. 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Keyword:
Artificial neural networks; Learning management systems; LENGUAJES Y SISTEMAS INFORMATICOS; Machine learning; Sentiment analysis; User experience; Virtual learning environments
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URL: https://doi.org/10.1007/s11063-020-10260-5
http://hdl.handle.net/10251/176382
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FACE-SAVING AND FACE-THREATENING NEGOTIATION BY LECTURERS: GENDER AND TEACHING EXPERIENCE DIFFERENCES
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In: Language Literacy: Journal of Linguistics, Literature, and Language Teaching, Vol 5, Iss 2, Pp 590-599 (2021) (2021)
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“THANK YOU, IT REALLY MAKES MY DAY”: COMPLIMENT RESPONSES REVISITED
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In: Language Literacy: Journal of Linguistics, Literature, and Language Teaching, Vol 5, Iss 2, Pp 321-331 (2021) (2021)
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Expériences migratoires de jeunes Ouest-Africains en France
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In: Voix Plurielles; Vol. 18 No. 1 (2021); 16-33 ; 1925-0614 (2021)
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Exploration of the Hollowness of the Modern Men in T.S. Eliot’s “The Hollow Men” (1925): An Analytical Approach
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In: Studies in Literature and Language; Vol 22, No 3 (2021): Studies in Literature and Language; 51-55 ; 1923-1563 ; 1923-1555 (2021)
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Adult English Language Learner Pathway to Literacy Initiative: Getting Learners to the Starting Line
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In: INTESOL Journal; Vol. 18 No. 1 (2021): Equity and Access For Language Learners: INTESOL Conference 2020 Showcase Issue; 51-74 ; 2373-8936 (2021)
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Motivation And The Young Writer: Reimagining John Dewey's Theory Of Experience
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In: Open Access Theses & Dissertations (2021)
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The Rhetorical Mediator: Understanding Agency In Indigenous Translation And Interpretation Through Indigenous Approaches To Ux
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In: Open Access Theses & Dissertations (2021)
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¡Sí Se Puede!: Understanding The Experiences Of Latina Students During Their Doctoral Journey At A Hispanic-Serving Institution
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In: Open Access Theses & Dissertations (2021)
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The Role of Translation in Multilingual User Experience
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In: Open Access Theses & Dissertations (2021)
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REASON, EXPERIENCE, AND LANGUAGE TO ACQUIRE KNOWLEDGE (IN WESTERN AND ISLAMIC PERSPECTIVES)
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In: LiNGUA: Jurnal Ilmu Bahasa dan Sastra; Vol 16, No 1 (2021): LiNGUA; 13 - 24 ; 2442-3823 ; 1693-4725 (2021)
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