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Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings
Evaluación automática de la personalidad a partir de las transliteraciones de los vlogs de YouTube mediante el uso de incrustaciones de palabras clásicas
DOI:
https://doi.org/10.15446/ing.investig.93803Keywords:
Personality, Word Embeddings, YouTube, Regression, Classification (en)Personalidad, Incrustaciones de Palabras, YouTube, Regresión, Clasificación (es)
The study of automatic personality recognition has gained attention in the last decade thanks to a variety of applications that derive from this field. The big five model (also known as OCEAN) constitutes a well-known method to label different personality traits. This work considers transliterations of video recordings collected from YouTube (originally provided by the Idiap research institute) and automatically generated scores for the five personality traits which also were provided in the database. The transliterations are modeled with two different word embedding approaches, Word2Vec and GloVe and three different levels of analysis are included: regression to predict the score of each personality trait, binary classification between strong vs. weak presence of each trait, and the tri-class classification according to three different levels of manifestations in each trait (low, medium, and high). According to our findings, the proposed approach provides similar results to others reported in the state-of-the-art. We think that further research is required to find better results. Our results, as well as others reported in the literature, suggest that there is a big gap in the study of personality traits based on linguistic patterns, which make it necessary to work on collecting and labeling data considering the knowledge of expert psychologists and psycholinguists.
El estudio del reconocimiento automático de la personalidad ha ganado atención en la última década gracias a las diversas aplicaciones que se derivan de este campo. El modelo de los cinco grandes (también conocido como OCEAN) constituye un método bien conocido para etiquetar diferentes rasgos de personalidad. En este trabajo se consideran transliteraciones de grabaciones de vídeo recogidas de YouTube (proporcionadas originalmente por el instituto de investigación Idiap) y puntuaciones generadas automáticamente para los cinco rasgos de personalidad que también se proporcionaron en la base de datos. Las transliteraciones se modelan con dos enfoques diferentes de incrustación de palabras, Word2Vec y GloVe, y se incluyen tres niveles diferentes de análisis: regresión para predecir la puntuación de cada rasgo de personalidad, clasificación binaria entre presencia fuerte y débil de cada rasgo, y la clasificación tri-clase según tres niveles diferentes de manifestaciones en cada rasgo (bajo, medio y alto). Según nuestros resultados, el enfoque propuesto proporciona resultados similares a otros reportados en el estado del arte. Creemos que es necesario seguir investigando para encontrar mejores resultados. Nuestros resultados, así como otros reportados en la literatura, sugieren que existe un gran vacío en el estudio de los rasgos de personalidad basados en patrones lingüísticos, lo que hace necesario trabajar en la recolección y etiquetado de datos considerando el conocimiento de psicólogos y psicolingüistas expertos.
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