Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings
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...
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| Format: | Article |
| Language: | English |
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Universidad Nacional de Colombia
2021-12-01
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| Series: | Ingeniería e Investigación |
| Subjects: | |
| Online Access: | https://revistas.unal.edu.co/index.php/ingeinv/article/view/93803 |
| _version_ | 1817986809826639872 |
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| author | Felipe Orlando López Pabón Juan Rafael Orozco Arroyave |
| author_facet | Felipe Orlando López Pabón Juan Rafael Orozco Arroyave |
| author_sort | Felipe Orlando López Pabón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:14:13Z |
| format | Article |
| id | doaj.art-50608fab2a214a9e92fd3aa27295de69 |
| institution | Directory Open Access Journal |
| issn | 0120-5609 2248-8723 |
| language | English |
| last_indexed | 2024-04-14T00:14:13Z |
| publishDate | 2021-12-01 |
| publisher | Universidad Nacional de Colombia |
| record_format | Article |
| series | Ingeniería e Investigación |
| spelling | doaj.art-50608fab2a214a9e92fd3aa27295de692022-12-22T02:23:11ZengUniversidad Nacional de ColombiaIngeniería e Investigación0120-56092248-87232021-12-01422e93803e9380310.15446/ing.investig.9380375970Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word EmbeddingsFelipe Orlando López Pabón0https://orcid.org/0000-0002-1209-6578Juan Rafael Orozco Arroyave1https://orcid.org/0000-0002-8507-0782Master student of Telecommunications Engineering and teaching assistantAssociate Professor at Universidad de AntioquiaThe 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.https://revistas.unal.edu.co/index.php/ingeinv/article/view/93803personalityword embeddingsyoutuberegressionclassification |
| spellingShingle | Felipe Orlando López Pabón Juan Rafael Orozco Arroyave Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings Ingeniería e Investigación personality word embeddings youtube regression classification |
| title | Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings |
| title_full | Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings |
| title_fullStr | Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings |
| title_full_unstemmed | Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings |
| title_short | Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings |
| title_sort | automatic personality evaluation from transliterations of youtube vlogs using classical and state of the art word embeddings |
| topic | personality word embeddings youtube regression classification |
| url | https://revistas.unal.edu.co/index.php/ingeinv/article/view/93803 |
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