Using Large Language Models to Improve Sentiment Analysis in Latvian Language

This empirical study explores the use of large language models (LLMs) in sentiment analysis and presents a new approach to creating a dataset in Latvian language using Reddit data. Using prompt engineering for the GPT-3.5-turbo model (latest at the time of writing), we achieved 82% accuracy that exceeds previous research on Latvian Tweet Sentiment Corpus by 50% in three class sentiment classification. We also demonstrate that LLMs can partially replace human labelers, making data set creation more cost-effective, especially for larger datasets. This work contributes to sentiment analysis in non-English languages, leveraging the power of LLMs. The paper introduces a new LVReddit dataset that contains more than 90000 samples, making it the largest available sentiment dataset for the Latvian language. Our findings confirm the LLM's underlying "understanding" of language. However, LLMs occasionally deviate from response templates, making parsing challenging. Future research should investigate fine-tuned models based on novel datasets and analyze language patterns.