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dc.contributor.authorArana Martínez, José María 
dc.contributor.authorGordillo León, Fernando
dc.contributor.authorDarias Plasencia, Jeannete
dc.contributor.authorMestas Hernández, Lilia
dc.date.accessioned2025-10-07T07:54:53Z
dc.date.available2025-10-07T07:54:53Z
dc.date.issued2020
dc.identifier.citationArana, J. M., Gordillo, F., Darias, J. y Méstas, L. (2020). Analysis of the efficacy and reliability of the Moodies app for detecting emotions through speech: does it actually work? Computers in Human Behavior, 104, 106156. https://doi.org/10.1016/j.chb.2019.106156es_ES
dc.identifier.issn0747-5632
dc.identifier.urihttp://hdl.handle.net/10366/167318
dc.description.abstract[EN] Apps are software programs that enable users to optimise their resources in different areas. Recent years have seen a huge increase in the number of apps, whose use has spread in step with their perceived efficacy and reliability. This research focused on the Moodies app, designed for the voice detection of the speaker's emotions. Yet does it actually gauge emotions, and does it do so consistently over time? Our study therefore used this app to analyse the soundtracks of 34 scenes from different films in four languages, and the output Moodies provided was recorded in a brief text in English, which was processed using the tool Linguistic Inquiry and Word Count (LIWC). The same procedure was then repeated for a second measure. The analysis of the correspondence between the results obtained with Moodies and the interpretation made by LIWC considered the variables Emotion, prompted by scenes in films (disgust, happiness, anger, fear, tenderness, and sadness), Language (English, Spanish, Italian, and French), and the time of the measurement (Listening 1 and 2); an analysis was also conducted of reliability and concurrent criterion validity. The results show that Moodies correctly analyses emotions in dimensional terms (positive vs negative emotion), but not so in categorical terms, as it has difficulties in distinguishing between the emotions of anger and sadness and those of fear and disgust. In terms of reliability, there was a good correlation between listenings (r's Pearson correlation coefficient = .977), albeit with differences in the percentage of words detected (Listening 1 - Listening 2), which ranged between 0.00 and 22.06 (absolute value). It was also noted that language is not a significant variable, although it identifies a higher percentage of emotion words in scenes of fear in Spanish than in any other language. Based on these data as a whole, it may be concluded that Moodies classifies emotion in a more general way than expected and desired.es_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAppes_ES
dc.subjectEmotionses_ES
dc.subjectLIWCes_ES
dc.subjectMoodieses_ES
dc.subjectEmotion recognitiones_ES
dc.subjectProsodyes_ES
dc.titleAnalysis of the efficacy and reliability of the Moodies app for detecting emotions through speech: Does it actually work?es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publishversionhttps://www.sciencedirect.com/science/article/pii/S0747563219303681?via%3Dihubes_ES
dc.subject.unesco61 Psicologíaes_ES
dc.identifier.doi10.1016/j.chb.2019.106156
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.journal.titleComputers in Human Behaviores_ES
dc.volume.number104es_ES
dc.page.initial106156es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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