Name
Abstract | ||
|---|---|---|
Event-specific twitter streams often reveal sudden spikes triggered by users’ upsurge of emotions to crucial moments in the real world. Although upsurge of emotion is usually identified by a sudden rise in the number of tweets, the detection for diverse event streams is not a trivial task. In this paper, we propose a new method to extract spiking tweets with upsurge of emotions based on characteristic expressions used in tweets. The core part of our method is to use a distant-supervised language model (Spike LM) built from tweets in spikes to capture such expressions. We investigate the performance of detecting emotional spiking tweets using language models including Spike LM. Our experimental results show that the natural language expressions used in emotional upsurge fit specifically well to Spike LM. |
Year | Venue | Field |
|---|---|---|
2015 | PACLIC | Expression (mathematics),Computer science,Natural language,Artificial intelligence,Natural language processing,Language model |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshinari Fujinuma | 1 | 0 | 1.35 |
| Hikaru Yokono | 2 | 17 | 4.36 |
| Pascual Martínez-Gómez | 3 | 61 | 7.36 |
| Akiko N. Aizawa | 4 | 678 | 120.63 |