Large Language Models for Sentiment Analysis to Detect Social Challenges: A Use Case with South African Languages
PositiveArtificial Intelligence
- Recent research has explored the application of large language models (LLMs) for sentiment analysis in South African languages, focusing on their ability to detect social challenges through social media posts. The study specifically evaluates the zero-shot performance of models like GPT-3.5, GPT-4, LlaMa 2, PaLM 2, and Dolly 2 in analyzing sentiment polarities across topics in English, Sepedi, and Setswana.
- This development is significant as it enables government departments to identify and address social issues more effectively by leveraging advanced AI technologies. The ability to analyze sentiment in multiple languages can enhance understanding of public opinion and improve responsiveness to community needs.
- The findings contribute to ongoing discussions about the role of AI in social sciences, particularly in multilingual contexts. The effectiveness of LLMs in diverse linguistic settings raises questions about their adaptability and accuracy, especially in low-resource languages, and highlights the importance of addressing potential biases and limitations in AI models.
— via World Pulse Now AI Editorial System
