  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.
