Direct Quotation Sentiment Analysis in Indonesian News Text using the IndoT5 Generative Models

Previous research on sentiment analysis of direct quotations in news articles utilized regex and Named Entity Recognition (NER) systems, but these methods often failed to capture the full context of the quotations. This paper introduces a generative approach that processes entire news documents to extract quotation sentences, speakers, targets, and sentiment polarity. By fine-tuning a generative model using a dataset annotated with GPT-4 and human annotators, and experimenting with regex for extraction, we demonstrate the effectiveness of this approach. The IndoT5-base-paraphrase model achieved impressive results, with F1 scores of 0.99 for quotation and speaker extraction, 0.74 for target extraction, and 0.81 for polarity analysis. These findings highlight the potential of combining generative models with regex for comprehensive sentiment analysis.