STF: Sentence Transformer Fine-Tuning For Topic Categorization With Limited Data

TL;DR

This paper introduces STF, a fine-tuning approach for pretrained Sentence Transformers to improve tweet topic classification, especially under limited labeled data conditions, outperforming existing methods.

Contribution

The paper presents a novel fine-tuning method for Sentence Transformers that enhances tweet topic classification accuracy with limited labeled data.

Findings

STF outperforms state-of-the-art approaches on benchmark datasets.

Effective with limited labeled data, reducing annotation costs.

Parameter sensitivity analysis optimized STF performance.

Abstract

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.