Text Sentiment Detection and Classification Based on Integrated Learning Algorithm


  • Zheng Lin Independent, China
  • Zeyu Wang University of California, Los Angeles, USA
  • Yue Zhu Independent, China
  • Zichao Li Canoakbit Alliance Inc., Canada
  • Hao Qin Independent, China




text emotion, integrated learning, machine learning


The aim of this paper is to explore the importance of textual sentiment detection in the field of Natural Language Processing and to classify and detect sentiment through various machine learning algorithms. Firstly, we train using Park Bayes, Random Forest, XGB and Support Vector Machine models, and then integrate them into a voting classifier for comparative analysis. The results show that the Random Forest model performs the best in the training set; and in both the validation set and the test set, the accuracy of the voting classifier is the highest, reaching 93.32% and 94.47%, respectively, which shows its superiority in the classification of text sentiment detection. Taken together, voting classifier has the best prediction results and provides an effective solution for text sentiment detection. This study not only provides an in-depth comparative analysis of the performance of different machine learning algorithms in text sentiment detection, but also provides a useful reference for subsequent related research and applications.


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How to Cite

Zheng Lin, Zeyu Wang, Yue Zhu, Zichao Li, & Hao Qin. (2024). Text Sentiment Detection and Classification Based on Integrated Learning Algorithm. Applied Science and Engineering Journal for Advanced Research, 3(3), 27–33. https://doi.org/10.5281/zenodo.11516191