Advancing Sentiment Analysis in Serbian Literature: A Zero and Few–Shot Learning Approach Using the Mistral Model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ARZNJLRXM" target="_blank" >RIV/00216208:11320/25:RZNJLRXM - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2024.clib-1.5" target="_blank" >https://aclanthology.org/2024.clib-1.5</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Advancing Sentiment Analysis in Serbian Literature: A Zero and Few–Shot Learning Approach Using the Mistral Model

Original language description

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%.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)

ISBN

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ISSN

2367-5578

e-ISSN

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Number of pages

13

Pages from-to

58-70

Publisher name

Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences

Place of publication

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Event location

Sofia, Bulgaria

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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