IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i18p3236-d908081.html
   My bibliography  Save this article

Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language

Author

Listed:
  • Drazen Draskovic

    (Department of Computer Science and Information Technology, School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia)

  • Darinka Zecevic

    (Department of Computer Science and Information Technology, School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia)

  • Bosko Nikolic

    (Department of Computer Science and Information Technology, School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia)

Abstract

In this research, a method of developing a machine model for sentiment processing in the Serbian language is presented. The Serbian language, unlike English and other popular languages, belongs to the group of languages with limited resources. Three different data sets were used as a data source: a balanced set of music album reviews, a balanced set of movie reviews, and a balanced set of music album reviews in English—MARD—which was translated into Serbian. The evaluation included applying developed models with three standard algorithms for classification problems (naive Bayes, logistic regression, and support vector machine) and applying a hybrid model, which produced the best results. The models were trained on each of the three data sets, while a set of music reviews originally written in Serbian was used for testing the model. By comparing the results of the developed model, the possibility of expanding the data set for the development of the machine model was also evaluated.

Suggested Citation

  • Drazen Draskovic & Darinka Zecevic & Bosko Nikolic, 2022. "Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3236-:d:908081
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/18/3236/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/18/3236/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Abhijit Bera & Mrinal Kanti Ghose & Dibyendu Kumar Pal, 2021. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(4), pages 1-12, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrei-Marius Avram & Verginica Barbu Mititelu & Vasile Păiș & Dumitru-Clementin Cercel & Ștefan Trăușan-Matu, 2023. "Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation," Mathematics, MDPI, vol. 11(11), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    2. Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
    3. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    4. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 56-65.
    5. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    6. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    7. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    8. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    9. Paul Brockman & Jim Cicon, 2013. "The Information Content Of Management Earnings Forecasts: An Analysis Of Hard Versus Soft Information," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 36(2), pages 147-174, June.
    10. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    11. Ton, Thai & Leung, Henry & Gao, Yang & Schiereck, Dirk, 2024. "Agreeing to disagree: Informativeness of sentiments in internet message boards," Pacific-Basin Finance Journal, Elsevier, vol. 87(C).
    12. Steven Heston & Nitish R. Sinha, 2016. "News versus Sentiment : Predicting Stock Returns from News Stories," Finance and Economics Discussion Series 2016-048, Board of Governors of the Federal Reserve System (U.S.).
    13. Depken II, Craig A. & Zhang, Ying, 2010. "Adverse selection and reputation in a world of cheap talk," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 548-558, November.
    14. Gao, Ziqing & Hua, Min & Liu, Heng & Yan, Chao, 2023. "News sentiment and CEO retirement: The impact on firm performance and risk," Research in International Business and Finance, Elsevier, vol. 66(C).
    15. Chu, Gang & Li, Xiao & Zhang, Yongjie, 2022. "Information demand and net selling around earnings announcement," Research in International Business and Finance, Elsevier, vol. 59(C).
    16. Zhenjie Wang & Jiewei Zhang & Hafeez Ullah, 2023. "Exploring the Multidimensional Perspective of Retail Investors’ Attention: The Mediating Influence of Corporate Governance and Information Disclosure on Corporate Environmental Performance in China," Sustainability, MDPI, vol. 15(15), pages 1-33, August.
    17. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    18. Tirunillai, S. & Tellis, G.J., 2011. "Does Online Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," ERIM Report Series Research in Management 25817, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    19. Domonkos F. Vamossy, 2024. "Social Media Emotions and Market Behavior," Papers 2404.03792, arXiv.org.
    20. Steffen Nauhaus & Johannes Luger & Sebastian Raisch, 2021. "Strategic Decision Making in the Digital Age: Expert Sentiment and Corporate Capital Allocation," Journal of Management Studies, Wiley Blackwell, vol. 58(7), pages 1933-1961, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3236-:d:908081. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.