Exploring the Effectiveness of Generative Languages in Sentiment Analysis Tasks in Brazilian Portuguese
Abstract
Large language models (LLMs) have been successfully applied in various natural language processing (NLP) tasks. This paper investigates their effectiveness in sentiment analysis tasks in the context of Brazilian Portuguese, exploring the identification of opinionated sentences, polarity, and comparative sentences. The study evaluates the performance of models such as ChatGPT and Sabiá on different tasks and datasets, comparing them with methods from the literature. Furthermore, we explore the use of LLMs in automatic data annotation. The results demonstrate the potential of LLMs in sentiment analysis, especially in polarity identification, and discuss their limitations and applications in data annotation tasks.
Copyright (c) 2024 Tiago de Melo, Gladson de Araújo, Carlos Maurício S. Figueiredo
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