Support #288
Label another 1000 tweets
| Status: | Closed | Start date: | 2010-06-05 | |
|---|---|---|---|---|
| Priority: | Normal | Due date: | ||
| Assignee: |  Luojun Qiu |
% Done: | 100% |
|
| Category: | - | Spent time: | 4.50 hours | |
| Target version: | - | Estimated time: | 5.00 hours |
Description
When relied on an emotional word list to label the tweets, we found that nearly 80% of tweets were labeled as neutral, 20% were labeled as containing positive emotion, and only about 1% were negative.
The dataset provided to you contains 500 tweets labeled as positive, and 500 tweets labeled as negative by a program that relays on the emotional word list. Please help to label these 1000 tweets into three categories (positive, negative, neutral) according to your judgment.
The emotions of some tweets are really ambiguous. When labeling, please relay on the literal meaning only and speed up the labeling process.
History
Updated by Luojun Qiu over 1 year ago
- Assignee set to Luojun Qiu
- % Done changed from 0 to 20
Updated by Luojun Qiu over 1 year ago
- % Done changed from 20 to 100
Updated by Kuiyu Chang over 1 year ago
- Status changed from New to Closed
student paid, closing issue.