Nowadays, topic classification from tweets attracts considerable research attention. Different classification systems have been suggested thanks to these research efforts. Nevertheless, they face major challenges owing to low performance metrics due to the limited amount of labeled data. We propose Sentence Transformers Fine-tuning (STF), a topic detection system that leverages pretrained Sentence Transformers models and fine-tuning to classify topics from tweets accurately. Moreover, extensive parameter sensitivity analyses were conducted to finetune STF parameters for our topic classification task to achieve the best performance results. Experiments on two benchmark datasets demonstrated that (1) the proposed STF can be effectively used for classifying tweet topics and outperforms the latest state-of-the-art approaches, and (2) the proposed STF does not require a huge amount of labeled tweets to achieve good accuracy, which is a limitation of many state-of-the-art approaches. Our main contribution is the achievement of promising results in tweet topic classification by applying pretrained sentence transformers language models.

使用预训练句子转换器模型进行微调的主题检测系统(STF)在从推文中准确分类主题方面具有很好的性能结果，并且不需要大量标记推文即可实现良好的准确性。

STF: 利用有限数据进行主题分类的句子转换器微调