@inproceedings{pichardo-estevez-etal-2023-i2c,
    title = "{I}2{C}-{H}uelva at {S}em{E}val-2023 Task 9: Analysis of Intimacy in Multilingual Tweets Using Resampling Methods and Transformers",
    author = "Pichardo Estevez, Abel  and
      Mata V{\'a}zquez, Jacinto  and
      Pach{\'o}n {\'A}lvarez, Victoria  and
      El Balima Cordero, Nordin",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.104",
    doi = "10.18653/v1/2023.semeval-1.104",
    pages = "758--762",
    abstract = "Nowadays, intimacy is a fundamental aspect of how we relate to other people in social settings. The most frequent way in which we can determine a high level of intimacy is in the use of certain emoticons, curse words, verbs, etc. This paper presents the approach developed to solve SemEval 2023 task 9: Multiligual Tweet Intimacy Analysis. To address the task, a transfer learning approach was conducted by fine tuning various pre-trained languagemodels. Since the dataset supplied by the organizer was highly imbalanced, our main strategy to obtain high prediction values was the implementation of different oversampling and undersampling techniques on the training set. Our final submission achieved an overall Pearson{'}s r of 0.497.",
}

Nowadays, intimacy is a fundamental aspect of how we relate to other people in social settings. The most frequent way in which we can determine a high level of intimacy is in the use of certain emoticons, curse words, verbs, etc. This paper presents the approach developed to solve SemEval 2023 task 9: Multiligual Tweet Intimacy Analysis. To address the task, a transfer learning approach was conducted by fine tuning various pre-trained languagemodels. Since the dataset supplied by the organizer was highly imbalanced, our main strategy to obtain high prediction values was the implementation of different oversampling and undersampling techniques on the training set. Our final submission achieved an overall Pearson{'}s r of 0.497.
Pichardo Estevez, Abel Mata V{\'a}zquez, Jacinto Pach{\'o}n {\'A}lvarez, Victoria El Balima Cordero, Nordin
I2C-Huelva at SemEval-2023 Task 9: Analysis of Intimacy in Multilingual Tweets Using Resampling Methods and Transformers