IMNTPU at the NTCIR-17 FinArg-1: Financial Argument-Based Sentiment Analysis and Argumentative Relations Identification in Social Media

In recent years, there has been a surge of interest in argument-based sentiment analysis and the identification of argumentative relationships in social media. These tasks encompass sentiment analysis of premises and claims, as well as the classification of argumentative relationships. Within these tasks, we have developed a fine-tuning method for transformer models. To evaluate and showcase this concept, we established a comprehensive framework to test and display the performance of BERT, RoBERTa, FinBERT, ALBERT, and GPT 3.5-turbo models on financial data and social media texts. Ultimately, the experimental results of these sub-tasks validate the effectiveness of our strategies. The primary contribution of our research is our proposal of two key elements: fine-tuning predominantly with BERT models and employing GPT for generative classification, aiming to enhance the identification of argumentative classifications. Through fine-tuning techniques, the state-of-the-art models can achieve better performance than the baseline.