TxPI-u: A Resource for Personality Identification of Undergraduates1footnote 11footnote 1Preprint of [24]. The final publication is available at IOS Press through https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169484
Table 10: Classification results using a ten fold cross validation technique with the stratified corpus of TxPI-u. Each result corresponds to a three-class classification problem. Results are given in F-score
['Words', 'Words 1-gram', 'Words 1-gram', 'Words 1-gram', 'Words 1-gram', 'Words 1-gram', 'Words 1-gram', 'Words 2-gram', 'Words 2-gram', 'Words 2-gram', 'Words 2-gram', 'Words 2-gram', 'Words 2-gram', 'Words 3-gram', 'Words 3-gram', 'Words 3-gram', 'Words 3-gram', 'Words 3-gram', 'Words 3-gram']
Altogether, we performed 405 experiments (5 traits, 9 representations, 3 weights schemes, and 3 learning algorithms) and for all of them we used 10 fold cross validation to evaluate each three-class classifier. Note that in the results’ table only boolean (bool) and term frequency (tf) as weighting schema is shown, that is because tf-idf (term frequency - inverse document frequency) performs similar to tf, therefore, we only show tf results. It is clear that these results are not definitive, and there still is an important room for improvement, e.g. a representation that combine open-vocabulary with word categories. Even more, a deeper analysis can be performed to better understand the difficulty of the personality identification problem.