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Determining Sentiment of Tweets Using First Bosnian Lexicon and (AnA)-Affirmative and Non-affirmative Words

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Advanced Technologies, Systems, and Applications V (IAT 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 142))

Abstract

The paper presents a model for sentiment tagging of Twitter messages based on (AnA)—affirmative and non-affirmative words and tagged sentiment dictionary/lexicon. Lexicon was used in combination with intensifiers, where intensifier (AnAword), that stand next to the word, produce more powerful (more positive or more negative) sentiment value of word—or the whole tweet. The result of the study was creating the first Bosnian sentiment lexicon that can be used for sentiment analysis of the Bosnian language. Besides that, the paper presents a more powerful way to measure sentimental value by using AnAwords. By using confusion matrix model’s overall accuracy was counted for both positive and negative sentiment value. Good overall evaluation of model was presented with huge values 0.9075 and 0.9004 for positive and negative sentiment value, respectively.

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Notes

  1. 1.

    In order to avoid going out of the road from given topic authors didn’t write proof of the given theorem in this article.

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Acknowledgements

The authors gratefully acknowledge the European Commission for funding the InnoRenewCoE project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund).

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Authors and Affiliations

  1. Faculty of Mathematics, Natural Science and Information Technologies, University of Primorska, Koper, Slovenia

    Sead Jahić & Jernej Vičič

Authors
  1. Sead Jahić
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  2. Jernej Vičič
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Corresponding author

Correspondence to Sead Jahić .

Editor information

Editors and Affiliations

  1. Faculty of electrical engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Samir Avdaković

  2. Department of Mathematics, Wellesley College, Wellesley Hills, MA, USA

    Ismar Volić

  3. Faculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina

    Aljo Mujčić

  4. Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Tarik Uzunović

  5. Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Adnan Mujezinović

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Jahić, S., Vičič, J. (2021). Determining Sentiment of Tweets Using First Bosnian Lexicon and (AnA)-Affirmative and Non-affirmative Words. In: Avdaković, S., Volić, I., Mujčić, A., Uzunović, T., Mujezinović, A. (eds) Advanced Technologies, Systems, and Applications V. IAT 2020. Lecture Notes in Networks and Systems, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-030-54765-3_25

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