3 items

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  Sentiment Analysis of Serbian Old Novels cite

  Ranka Stanković, Miloš Košprdić, Milica Ikonić Nešić, Tijana Radović (2022)

  In this paper we present first study of Sentiment Analysis (SA) of Serbian novels from the 1840-1920 period. The preparation of sentiment lexicon was based on three existing lexicons: NRC, AFFIN and Bing with additional extensive corrections. The first phase of dataset refinement included filtering the word that are not found in Serbian morphological dictionary and in second automatic POS tagging and lemma were manually corrected. The polarity lexicon was extracted and transformed into ontolex-lemon and published as initial ...

  sentiment lexicon, sentiment analysis, distant-reading, machine learning, old novels

  Ranka Stanković, Miloš Košprdić, Milica Ikonić Nešić, Tijana Radović. "Sentiment Analysis of Serbian Old Novels" in Proceedings of the 2nd Workshop on Sentiment Analysis and Linguistic Linked Data, June 2022, Marseille, France, European Language Resources Association (2022)

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  Fourth Summer Datathon on Linguistic Linked Open Data cite

  Tijana Radović, Ranka Stanković (2023)

  The 4th Summer Datathon on Linguistic Linked Open Data (SD-LLOD-22) was held in Spain, in Cersedilla near Madrid, in May 2022, and organized by the COST Action NexusLinguarum. The school gathered interested researchers, academics, students who wanted to acquire and/or expand their knowledge in the field of linguistic linked data science. During the school, a spectrum of topics from the field of linked data was presented, from various ontologies, through document integration, annotation and natural language text processing tools ...

  linguistic linked open data, sentiment analysis, linked data, RDF

  Tijana Radović, Ranka Stanković. "Fourth Summer Datathon on Linguistic Linked Open Data" in Infotheca, Faculty of Philology, University of Belgrade (2023). https://doi.org/10.18485/infotheca.2023.23.1.6

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

  Milica Ikonić Nešić, Saša Petalinkar, Mihailo Škorić, Ranka Stanković, Biljana Rujević (2024)

  Ova studija predstavlja analizu sentimenta srpskih starih romana iz perioda 1840-1920, koristeći veliki jezički model (LLM) Mistral za tehniku učenja sa zasnovani na takozvanim "zero" i "few-shot" pokušajima. Glavni pristup uvodi inovacije osmišljavanjem istraživačkih upita (promptova) uključuju tekst sa uputstvom za klasifikaciju bez primera i na osnovu nekoliko primera, omogućavajući jezičkom modelu da klasifikuje osećanja u pozitivne, negativne ili objektivne kategorije. Ova metodologija ima za cilj da pojednostavi analizu osećanja ograničavanjem odgovora, čime se povećava preciznost ...

  zero-shot, few-shot, sentiment, Serbian, Mistral model

  Milica Ikonić Nešić, Saša Petalinkar, Mihailo Škorić, Ranka Stanković, Biljana Rujević. "Advancing Sentiment Analysis in Serbian Literature: A Zero and Few-Shot Learning Approach Using the Mistral Model" in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, Sofia, Bulgaria, 9-10 September 2024, LREC | COLING (2024)