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Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity

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Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity

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dc.contributor.author Melnyk, Lidiia es_ES
dc.contributor.author Feld, Linda es_ES
dc.date.accessioned 2023-01-09T08:29:28Z
dc.date.available 2023-01-09T08:29:28Z
dc.date.issued 2022-11-23
dc.identifier.uri http://hdl.handle.net/10251/191102
dc.description.abstract [EN] This paper explores different options of detecting the stance of German YouTube comments regarding the topic of gender diversity and compares the respective results with those of sentiment analysis, showing that these are two very different NLP tasks focusing on distinct characteristics of the discourse. While an already existing model was used to analyze the comments sentiment (BERT), the comments stance was first annotated and then used to train different models SVM with TF-IDF, DistilBERT, LSTM and CNN for predicting the stance of unseen comments. The best results were achieved by the CNN, reaching 78.3% accuracy (92% after dataset normalization) on the test set. Whereas the most common stance identified in the comments is a neutral one (neither completely in favor nor completely against gender diversity), the overall sentiment of the discourse turns out to be negative. This shows that the discourse revolving around the topic of gender diversity in YouTube comments is filled with strong opinions, on the one hand, but also opens up a space for anonymously inquiring and learning about the topic and its implications, on the other. Our research thereby (1) contributes to the understanding and application of different NLP tasks used to predict the sentiment and stance of unstructured textual data, and (2) provides relevant insights into society s attitudes towards a changing system of values and beliefs. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Journal of Computer-Assisted Linguistic Research es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Stance detection es_ES
dc.subject Sentiment analysis es_ES
dc.subject BERT es_ES
dc.subject Neural networks es_ES
dc.subject Annotation es_ES
dc.subject YouTube comments es_ES
dc.subject Gender diversity es_ES
dc.title Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/jclr.2022.18224
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Melnyk, L.; Feld, L. (2022). Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity. Journal of Computer-Assisted Linguistic Research. 6:59-86. https://doi.org/10.4995/jclr.2022.18224 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/jclr.2022.18224 es_ES
dc.description.upvformatpinicio 59 es_ES
dc.description.upvformatpfin 86 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.identifier.eissn 2530-9455
dc.relation.pasarela OJS\18224 es_ES
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