This project is the code for SemEval-2016 Task 4 sub-task C competition.
Task: Given a tweet known to be about a given topic, estimate the sentiment conveyed by the tweet towards the topic on a five-point scale.
| Name | Size | # Available (Percentage) | # Topics | #(-2) | #(-1) | #(0) | #(1) | #(2) | Max Length | Min Length | Average Length |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Gold Train | 6000 | 5346 (89.1%) | 60 | 87 | 668 | 1654 | 3154 | 437 | 34 | 5 | 19.49 |
| Gold Dev | 2000 | 1795 (89.75%) | 20 | 43 | 296 | 675 | 933 | 53 | 31 | 6 | 19.58 |
| Gold Devtest | 2000 | 1781 (89.05%) | 20 | 31 | 233 | 583 | 1005 | 148 | 31 | 5 | 19.69 |
| Input Devtest | 2000 | 1781 (89.05%) | 20 | - | - | - | - | - | 31 | 5 | 19.69 |
Note: The "Not Available" terms are removed when gather statistics information of Max Length, Min Length, Average Length.
Number and Sentiment Intensity mapping method
| Sentiment Intensity | strongly negative | negative | negative or neutral | positive | strongly positive |
|---|---|---|---|---|---|
| Sentiment Score | -2 | -1 | 0 | 1 | 2 |
The topics in gold train, gold dev, gold devtest data: topics. We can see that the topic in different data set is different. Exactly, there are no common topics between the three data set.
- Yunchao He
- yunchaohe@gmail.com
- YZU at Taiwan
- @元智大学资讯工程学系1608B 民105年1月