From classification to quantification in tweet sentiment analysis
Bibliographic Data
| ID | 4614779 |
|---|---|
| Authors | Wei Gao (0000-0003-3999-7146, Hamad bin Khalifa University), Fabrizio Sebastiani (0000-0003-4221-6427, Hamad bin Khalifa University, corresponding author) |
| Year | 2016 |
| Volume | 6 |
| Issue | 1 |
| Publication date | 2016-12-01 |
| 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/s13278-016-0327-z |
| OpenAlex | W2342355650 |
| Language | EN |
| Citations received | 3 |
| References cited | 50 |
Information retrieval · Natural language processing · Sentiment analysis · Advanced Text Analysis Techniques · Computer Science · Sentiment Analysis and Opinion Mining · Topic Modeling · Artificial Intelligence
Elements of Information Theory
Libsvm
Temporal Patterns of Happiness and Information in a Global Social Network
Twitter mood predicts the stock market
Individual Comparisons by Ranking Methods
Predicting the Future with Social Media
Maximum Likelihood from Incomplete Data Via the EM Algorithm
From Tweets to Polls
A Method of Automated Nonparametric Content Analysis for Social Science
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,43 |
| Citation span | 2019 - 2021 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |