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

Xuan Liu, Tianyi Shi, Guohui Zhou, Mingzhe Liu, Zhengtong Yin, Lirong Yin and Wenfeng Zheng ()
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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

Palgrave Communications, 2023, vol. 10, issue 1, 1-9

Abstract: 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.

Date: 2023
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DOI: 10.1057/s41599-023-01816-6

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