Emotion classification for short texts
An improved multi-label method
Bibliographic Data
| ID | 22236346 |
|---|---|
| Authors | Xuan Liu (0000-0001-5599-2607, University of Electronic Science and Technology of China), Tianyi Shi (0000-0002-1278-8507, University of Electronic Science and Technology of China), Guohui Zhou (0000-0002-4500-1002, University of Electronic Science and Technology of China), Mingzhe Liu (0000-0001-7054-997X, Wenzhou University), Zhengtong Yin (0000-0002-9818-9205, Guizhou University), Lirong Yin (0000-0002-5022-610X, Louisiana State University), Wenfeng Zheng (0000-0002-8486-1654, University of Electronic Science and Technology of China, corresponding author) |
| Year | 2023 |
| Volume | 10 |
| Issue | 1 |
| Publication date | 2023-06-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Humanities and Social Sciences Communications (JOURNAL) |
| Journal identifiers | ISSN: 2662-9992 • E-ISSN: 2662-9992 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1057/s41599-023-01816-6 |
| OpenAlex | W4379882795 |
| Language | EN |
| Citations received | 7 |
| References cited | 37 |
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
Data mining · Machine learning · Natural language processing · Precision and recall · Sentence · Sentiment analysis · Advanced Text Analysis Techniques · Computer Science · Sentiment Analysis and Opinion Mining · Text and Document Classification Technologies · Artificial Intelligence
Interpreting Black-Box Models
Exploring excitement counterbalanced by concerns towards AI technology using a descriptive-prescriptive data processing method
Social media platform-oriented topic mining and information security analysis by big data and deep convolutional neural network
A thorough examination of smart city applications
The Impact of Language Translation on Plagiarism Rates
Exploring the complexities of CSR and firm performances
Sustaining company performance during the war-induced crisis using sourcing capability and substitute input
| Unique citing works | 7 |
|---|---|
| Citations per year | 2,33 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 7 |