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Machine learning framework for country image analysis

Author

Listed:
  • Luis N. Zúñiga-Morales

    (Universidad Iberoamericana, Ciudad de México)

  • Jorge Ángel González-Ordiano

    (Universidad Iberoamericana, Ciudad de México)

  • J. Emilio Quiroz-Ibarra

    (Universidad Iberoamericana, Ciudad de México)

  • César Villanueva Rivas

    (Universidad Iberoamericana, Ciudad de México)

Abstract

In this work, we compare the performance of a machine learning framework based on a support vector machine (SVM) with fastText embeddings, and a Deep Learning framework consisting on fine-tuning Large Language Models (LLMs) like Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, and Twitter roBERTa Base, to automate the classification of text data to analyze the country image of Mexico in selected data sources, which is described using 18 different classes, based in International Relations theory. To train each model, a data set consisting of tweets from relevant selected Twitter accounts and news headlines from The New York Times is used, based on an initial manual classification of all the entries. However, the data set presents issues in the form of imbalanced classes and few data. Thus, a series of text augmentation techniques are explored: gradual augmentation of the eight less represented classes and an uniform augmentation of the data set. Also, we study the impact of hashtags, user names, stopwords, and emojis as additional text features for the SVM model. The results of the experiments indicate that the SVM reacts negatively to all the data augmentation proposals, while the Deep Learning one shows small benefits from them. The best result of 52.92%, in weighted-average $$F_1$$ F 1 score, is obtained by fine-tuning the Twitter roBERTa Base model without data augmentation.

Suggested Citation

  • Luis N. Zúñiga-Morales & Jorge Ángel González-Ordiano & J. Emilio Quiroz-Ibarra & César Villanueva Rivas, 2024. "Machine learning framework for country image analysis," Journal of Computational Social Science, Springer, vol. 7(1), pages 523-547, April.
  • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-023-00246-3
    DOI: 10.1007/s42001-023-00246-3
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    References listed on IDEAS

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    1. Dharen Kumar Pandey & Rahul Kumar, 2023. "Russia-Ukraine War and the global tourism sector: A 13-day tale," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(5), pages 692-700, March.
    2. Herrmann, Richard K. & Fischerkeller, Michael P., 1995. "Beyond the enemy image and spiral model: cognitive–strategic research after the cold war," International Organization, Cambridge University Press, vol. 49(3), pages 415-450, July.
    3. Enrique Marinao-Artigas & Karla Barajas-Portas, 2021. "A Cross-Destination Analysis of Country Image: A Key Factor of Tourism Marketing," Sustainability, MDPI, vol. 13(17), pages 1-20, August.
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