Topics extraction in incremental short texts based on LSTM
Bibliographic Data
| ID | 4694607 |
|---|---|
| Authors | Xubo Zhang (0000-0002-2912-2998, University of International Business and Economics, corresponding author), Li Zhang (0000-0002-4441-5131, University of International Business and Economics) |
| 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/s13278-020-00699-8 |
| OpenAlex | W3092455543 |
| Language | EN |
| Citations received | 1 |
| References cited | 24 |
Cluster analysis · Information retrieval · Linguistics · Machine learning · Natural language processing · Social media · Topic model · Word2vec · World Wide Web · Advanced Text Analysis Techniques · Computer Science · Topic Modeling · Web Data Mining and Analysis · Artificial Intelligence
Principal component analysis
Long Short-Term Memory
Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube
Finding Structure in Time
Neutrality may matter
A lightweight and multilingual framework for crisis information extraction from Twitter data
On detecting urgency in short crisis messages using minimal supervision and transfer learning
Deep learning CNN-LSTM framework for Arabic sentiment analysis using textual information shared in social networks
A systematic mapping on automatic classification of fake news in social media
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |