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        <title>Advancing Sentiment Analysis in Serbian Literature: A Zero and Few–Shot Learning Approach Using the Mistral Model</title>
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    <abstract>This study presents the Sentiment Analysis of the Serbian old novels from the 1840-1920 period, employing the Mistral Large Language Model (LLM) to pioneer zero and few-shot learning techniques. The main approach innovates by devising research prompts that include guidance text for zero-shot classification and examples for few-shot learning, enabling the LLM to classify sentiments into positive, negative, or objective categories. This methodology aims to streamline sentiment analysis by limiting responses, thereby enhancing classification precision. Python, along with the Hugging Face Transformers and LangChain libraries, serves as our technological backbone, facilitating the creation and refinement of research prompts tailored for sentence-level sentiment analysis. The results of sentiment analysis in both scenarios, zero-shot and few-shot, have indicated that the zero-shot approach outperforms, achieving an accuracy of 68.2%.</abstract>
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%0 Conference Proceedings
%T Advancing Sentiment Analysis in Serbian Literature: A Zero and Few–Shot Learning Approach Using the Mistral Model
%A Nešić, Milica Ikonić
%A Petalinkar, Saša
%A Škorić, Mihailo
%A Stanković, Ranka
%A Rujević, Biljana
%S Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)
%D 2024
%8 September
%I Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
%C Sofia, Bulgaria
%F nesic-etal-2024-advancing
%X This study presents the Sentiment Analysis of the Serbian old novels from the 1840-1920 period, employing the Mistral Large Language Model (LLM) to pioneer zero and few-shot learning techniques. The main approach innovates by devising research prompts that include guidance text for zero-shot classification and examples for few-shot learning, enabling the LLM to classify sentiments into positive, negative, or objective categories. This methodology aims to streamline sentiment analysis by limiting responses, thereby enhancing classification precision. Python, along with the Hugging Face Transformers and LangChain libraries, serves as our technological backbone, facilitating the creation and refinement of research prompts tailored for sentence-level sentiment analysis. The results of sentiment analysis in both scenarios, zero-shot and few-shot, have indicated that the zero-shot approach outperforms, achieving an accuracy of 68.2%.
%U https://aclanthology.org/2024.clib-1.5
%P 58-70

Markdown (Informal)

[Advancing Sentiment Analysis in Serbian Literature: A Zero and Few–Shot Learning Approach Using the Mistral Model](https://aclanthology.org/2024.clib-1.5) (Nešić et al., CLIB 2024)

• Advancing Sentiment Analysis in Serbian Literature: A Zero and Few–Shot Learning Approach Using the Mistral Model (Nešić et al., CLIB 2024)

ACL

• Milica Ikonić Nešić, Saša Petalinkar, Mihailo Škorić, Ranka Stanković, and Biljana Rujević. 2024. Advancing Sentiment Analysis in Serbian Literature: A Zero and Few–Shot Learning Approach Using the Mistral Model. In Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024), pages 58–70, Sofia, Bulgaria. Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences.