<p>Social media is flooded with a large volume of slang. Whilst many scholars have focused on the impact of slang through sentiment analysis, few have paid attention to the emotions represented by domain-specific slang. This study takes cat-related slang as an example to construct the CatSlang-SA dataset and evaluates its effectiveness in sentiment analysis tasks. The dataset consists of 10,025 posts from Reddit, each containing cat-related slang from the Urban Dictionary. The dataset is annotated with three sentiment labels: positive (1,545 instances), negative (2,528 instances), and neutral (5,952 instances). The dataset’s suitability is evaluated by the term frequency–inverse document frequency (TF-IDF) vectorisation technique and pre-trained transformer models, including four classical machine learning models and four pre-trained models. The results of the paired bootstrap test show that the DistilBERT-base-cased model achieved the best performance on the test set, with a Macro-F1 score of 0.9483. The transfer-comparative experiment demonstrates that, at the category level, the DistilBERT-base-cased model fine-tuned on CatSlang-SA significantly outperforms the model fine-tuned on the relevant portions of the Multi-Source, Multi-Language Social Media Dataset in the positive category. The main contribution of this study lies in constructing the first sentiment analysis dataset related to cat slang, providing a new resource for the study of domain-specific slang, and highlighting the role of domain-specific corpora in improving model performance.</p>