IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-01816-6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-01816-6
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-023-01816-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).
    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. 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.
    2. 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.
    3. 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.
    4. Wang, Changlin, 2024. "Social media platform-oriented topic mining and information security analysis by big data and deep convolutional neural network," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    5. 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.
    6. Tian, Li & Wang, Qianyun, 2024. "Improving mineral mining enterprises environmental performance through corporate social responsibility practices in China: Implications for minerals policymaking," Resources Policy, Elsevier, vol. 88(C).

    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. Kitti Nagy & Jozef Kapusta, 2021. "Improving fake news classification using dependency grammar," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.
    2. Farhan Shehzad & Abdur Rehman & Kashif Javed & Khalid A. Alnowibet & Haroon A. Babri & Hafiz Tayyab Rauf, 2022. "Binned Term Count: An Alternative to Term Frequency for Text Categorization," Mathematics, MDPI, vol. 10(21), pages 1-25, November.
    3. Masood, Muhammad Ali & Abbasi, Rabeeh Ayaz, 2021. "Using graph embedding and machine learning to identify rebels on twitter," Journal of Informetrics, Elsevier, vol. 15(1).

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01816-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.