Model in Distress: Sentiment Analysis on French Synthetic Social Media
Automated analysis of customer feedback on social media is hindered by three challenges: the high cost of annotated training data, the scarcity of evaluation sets, especially in multilingual settings, and privacy concerns that prevent data sharing and reproducibility. We address these issues by developing a generalizable synthetic data generation pipeline applied to a case study on customer distress detection in French public transportation. Our approach utilizes backtranslation with fine-tuned models to generate 1.7 million synthetic tweets from a small seed corpus, complemented by synthetic reasoning traces. We train 600M-parameter reasoners with English and French reasoning that achiev...
| Authors | Pierre-Carl Langlais, Pavel Chizhov, Yannick Detrois et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 April 2026 |
| Last updated at source | 5 October 2026 |
Models and datasets referencing this paper
- PleIAs/data_samplesDataset
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