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Research on Migrant Works' Concern Recognition and Emotion Analysis Based on Web Text Data
Zhijie Dou1, Zixuan Cheng2, Dongmei Huang3
1Shool of Management, Changchun University, Changchun, China.
Frontiers in Psychology
|September 17, 2021
Summary
Online self-media reveals migrant workers
Area of Science:
- Social Sciences
- Computational Linguistics
- Digital Sociology
Background:
- Online self-media offers migrant workers convenience, autonomy, and equality.
- It serves as a crucial platform for observing society and expressing demands.
Purpose of the Study:
- To analyze migrant workers' concerns and emotions using online data.
- To develop a model for recognizing and understanding migrant workers' key issues.
Main Methods:
- Data crawling from Weibo on migrant worker topics.
- TF-IDF and Word2Vec for concern recognition model construction.
- Deep learning (Bi-LSTM, CNN) for emotion analysis.
Main Results:
- Key concerns include wages, children's education, medical care, and returning home.
- Negative emotions (worries, complaints) significantly outweigh positive ones.
- Concentration of negative emotions correlates with social events.
Conclusions:
- Web text data effectively captures and predicts migrant workers' concerns and emotions.
- Core well-being issues in urban integration remain largely unsolved.
- Targeted government intervention is needed to address migrant workers' needs.
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