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Emotion classification for short texts: an improved multi-label method

Author

Listed:
  • Xuan Liu

    (University of Electronic Science and Technology of China)

  • Tianyi Shi

    (University of Electronic Science and Technology of China)

  • Guohui Zhou

    (University of Electronic Science and Technology of China)

  • Mingzhe Liu

    (Wenzhou University of Technology)

  • Zhengtong Yin

    (Guizhou University)

  • Lirong Yin

    (Louisiana State University)

  • Wenfeng Zheng

    (University of Electronic Science and Technology of China)

Abstract

The process of computationally identifying and categorizing opinions expressed in a piece of text is of great importance to support better understanding and services to online users in the digital environment. However, accurate and fast multi-label automatic classification is still insufficient. By considering not only individual in-sentence features but also the features in the adjacent sentences and the full text of the tweet, this study adjusted the Multi-label K-Nearest Neighbors (MLkNN) classifier to allow iterative corrections of the multi-label emotion classification. It applies the new method to improve both the accuracy and speed of emotion classification for short texts on Twitter. By carrying out three groups of experiments on the Twitter corpus, this study compares the performance of the base classifier of MLkNN, the sample-based MLkNN (S-MLkNN), and the label-based MLkNN (L-MLkNN). The results show that the improved MLkNN algorithm can effectively improve the accuracy of emotion classification of short texts, especially when the value of K in the MLkNN base classifier is 8, and the value of α is 0.7, and the improved L-MLkNN algorithm outperforms the other methods in the overall performance and the recall rate reaches 0.8019. This study attempts to obtain an efficient classifier with smaller training samples and lower training costs for sentiment analysis. It is suggested that future studies should pay more attention to balancing the efficiency of the model with smaller training sample sizes and the completeness of the model to cover various scenarios.

Suggested Citation

  • Xuan Liu & Tianyi Shi & Guohui Zhou & Mingzhe Liu & Zhengtong Yin & Lirong Yin & Wenfeng Zheng, 2023. "Emotion classification for short texts: an improved multi-label method," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01816-6
    DOI: 10.1057/s41599-023-01816-6
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    References listed on IDEAS

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    1. Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).
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    Cited by:

    1. Ramachandramoorthi Shanmugapriya & Perichetla Kandaswamy Hemalatha & Lenka Cepova & Jiri Struz, 2023. "A Study of Independency on Fuzzy Resolving Sets of Labelling Graphs," Mathematics, MDPI, vol. 11(16), pages 1-9, August.
    2. Zahra Amiri & Arash Heidari & Mehdi Darbandi & Yalda Yazdani & Nima Jafari Navimipour & Mansour Esmaeilpour & Farshid Sheykhi & Mehmet Unal, 2023. "The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors," Sustainability, MDPI, vol. 15(16), pages 1-41, August.
    3. Shanmugam Jagan & Ashish Ashish & Miroslav Mahdal & Kenneth Ruth Isabels & Jyoti Dhanke & Parita Jain & Muniyandy Elangovan, 2023. "A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms," Mathematics, MDPI, vol. 11(13), pages 1-21, June.
    4. Mi Zou & Peng Liu & Xuan Wu & Wei Zhou & Yuan Jin & Meiqi Xu, 2023. "Cognitive Characteristics of an Innovation Team and Collaborative Innovation Performance: The Mediating Role of Cooperative Behavior and the Moderating Role of Team Innovation Efficacy," Sustainability, MDPI, vol. 15(14), pages 1-23, July.

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