Context-Aware Civil Unrest Event Prediction Using Neutrosophic-Aspect-Based Sentiment Analysis, PSO, and Hierarchical LSTM
Bibliographic Data
| ID | 22106925 |
|---|---|
| Authors | Pratima Singh (0000-0002-0407-618X, Netaji Subhas University of Technology), Amita Jain (0000-0003-3195-0220, Netaji Subhas University of Technology) |
| Year | 2024 |
| Volume | 11 |
| Issue | 3 |
| Pages | 3667-3677 |
| Publication date | 2024-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3338509 |
| OpenAlex | W4390357607 |
| Language | EN |
| Citations received | 1 |
| References cited | 24 |
Civil unrest is among the important hurdles in the countries’ progress as it deteriorates gross domestic product (GDP), international relations, foreign direct investment (FDI), globalization, public opinion, tourism, and businesses. Due to civil unrest a variety of serious problems, viz. loss of life/injury, resources, political stability, and human rights occur. Recently, few researchers have given insights on the prediction of occurrences of civil unrest events by using hypothesis testing and some basic machine/deep learning models. Important factors such as people’s emotions/sentiments, contextual information, and civil unrest events feature’ importance are ignored presently. For the first time, the proposed work overcomes all these research gaps by hybridizing the neutrosophic set, aspect-based sentiment analysis, particle swarm optimization (PSO), and hierarchical long short-term memory (hierarchical LSTM). Neutrosophic set along with aspect-based sentiment analysis has been used to get the sentiment and features’ importance. The resulting features’ weights have been optimized using PSO. For a more comprehensive understanding of the input sequence and feature weights, hierarchical LSTM has been used. Doing so obtained results that are more accurately improved for civil unrest events prediction. The performance of the proposed model has been evaluated and compared with state of art methods. Experimentation and evaluation show the proposed model outperforms the baseline methods by 3% to 15%on the standard datasets in terms of accuracy
Geography · Machine learning · Political science · Politics · Sentiment analysis · Unrest · Computer Science · Law · Organizational and Employee Performance · Sentiment Analysis and Opinion Mining · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |