Using a self-attention architecture to automate valence categorization of French teenagers’ free descriptions of their family relationships. A proof of concept

This paper proposes a proof of concept of using natural language processing techniques to categorize valence of family relationships described in free texts written by french teenagers. The proposed study traces the evolution of techniques for word embedding. After decomposing the different texts in our possession into short texts composed of sentences and manual labeling, we tested different word embedding scenarios to train a multi-label classification model where a text can take several labels : labels describing the family link between the teenager and the person mentioned in the text and labels describing the teenager’s relationship with them positive/negative/neutral valence). The natural baseline for word vector representation of our texts is to build a TF-IDF and train classical classifiers (Elasticnet logistic regression, gradient boosting, random forest, support vector classifier) after selecting a model by cross validation in each class of machine learning models. We then studied the strengths of word-vectors embeddings by an advanced language representation technique via the CamemBERT transformer model, and, again, used them with classical classifiers to compare their respective performances. The last scenario consisted in augmenting the CamemBERT with output dense layers (perceptron) representing a classifier adapted to the multi-label classification and fine-tuning the CamemBERT original layers. The optimal fine-tuning depth that achieves a bias-variance trade-off was obtained by a cross-validation procedure. The results of the comparison of the three scenarios on a test dataset show a clear improvement of the classification performances of the scenario with fine-tuning beyond the baseline and of a simple vectorization using CamemBERT without fine-tuning. Despite the moderate size of the dataset and the input texts, fine-tuning to an optimal depth remains the best solution to build a classifier. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study reports an additional analysis of data collected and published in Falissard, B. et al. (2015). PLOS ONE, 10(7), doi.org/10.1371/journal.pone.0132153 which obtained ethical agreement from the CCTIRS, CNIL (national IRB for this kind of study) with number MG/CP 10962. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Computer code is available online.