A deep learning-based social media text analysis framework for disaster resource management
Bibliographic Data
| ID | 4697419 |
|---|---|
| Authors | Ashutosh Bhoi (0009-0001-6789-2715, International Institute of Information Technology), Sthita Pragyan Pujari (0009-0009-0191-3059, International Institute of Information Technology), Rakesh Chandra Balabantaray (0000-0003-0995-0013, International Institute of Information Technology, corresponding author) |
| Year | 2020 |
| Volume | 10 |
| Issue | 1 |
| Publication date | 2020-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-020-00692-1 |
| OpenAlex | W3086203873 |
| Language | EN |
| Citations received | 5 |
| References cited | 45 |
Deep learning · Embedding · Machine learning · Natural language processing · Sliding window protocol · Social media · Transfer of learning · Word embedding · World Wide Web · Computer Science · Public Relations and Crisis Communication · Sentiment Analysis and Opinion Mining · Topic Modeling · Artificial Intelligence
Learning representations by back-propagating errors
Nearest neighbor pattern classification
Long Short-Term Memory
The limits of crisis data
Challenges in Social Network Research
A survey of event detection techniques in online social networks
A fine-grained social network recommender system
Character level embedding with deep convolutional neural network for text normalization of unstructured data for Twitter sentiment analysis
Ranking and grouping social media requests for emergency services using serviceability model
Disaster damage assessment from the tweets using the combination of statistical features and informative words
A novel framework based on bi-objective optimization and LAN2FIS for Twitter sentiment analysis
Deep learning approaches for Arabic sentiment analysis
| Unique citing works | 5 |
|---|---|
| Citations per year | 1 |
| Citation span | 2021 - 2022 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 5 |