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  • Impact of negation and ana-words on overall sentiment value of the text written in the Bosnian language [Elektronski vir]
    Jahić, Sead ; Vičič, Jernej
    first_pagesettingsOrder Article Reprints Open AccessArticle Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language by Sead Jahić 1,*ORCID andJernej ... Vičič 1,2ORCID 1 Faculty of Mathematics, Natural Science and Information Technologies, University of Primorska, 6000 Koper, Slovenia 2 Research Centre of the Slovenian Academy of Science and Arts, The Fran Ramovš Institute, 1000 Ljubljana, Slovenia * Author to whom correspondence should be addressed. Appl. Sci. 2023, 13(13), 7760; https://doi.org/10.3390/app13137760 Received: 9 June 2023 / Revised: 25 June 2023 / Accepted: 26 June 2023 / Published: 30 June 2023 (This article belongs to the Special Issue Natural Language Processing (NLP) and Applications) Download Browse Figures Versions Notes Abstract In this manuscript, we present our efforts to develop an accurate sentiment analysis model for Bosnian-language tweets which incorporated three elements: negation cues, AnA-words (referring to maximizers, boosters, approximators, relative intensifiers, diminishers, and minimizers), and sentiment-labeled words from a lexicon. We used several machine-learning techniques, including SVM, Naive Bayes, RF, and CNN, with different input parameters, such as batch size, number of convolution layers, and type of convolution layers. In addition to these techniques, BOSentiment is used to provide an initial sentiment value for each tweet, which is then used as input for CNN. Our best-performing model, which combined BOSentiment and CNN with 256 filters and a size of 4×4 , with a batch size of 10, achieved an accuracy of over 92% . Our results demonstrate the effectiveness of our approach in accurately classifying the sentiment of Bosnian tweets using machine-learning techniques, lexicons, and pre-trained models. This study makes a significant contribution to the field of sentiment analysis for under-researched languages such as Bosnian, and our approach could be extended to other languages and social media platforms to gain insight into public opinion.
    Vir: Applied sciences [Elektronski vir]. - ISSN 2076-3417 (Vol. 13, iss. 13, art. 7760, 2023, str. 1-24)
    Vrsta gradiva - e-članek ; neleposlovje za odrasle
    Leto - 2023
    Jezik - angleški
    COBISS.SI-ID - 160089347

    Povezava(-e):

    https://www.mdpi.com/2076-3417/13/13/7760
    http://dx.doi.org/10.3390/app13137760
    DOI

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