Update README.md
Browse files
README.md
CHANGED
|
@@ -38,3 +38,26 @@ configs:
|
|
| 38 |
- split: test
|
| 39 |
path: data/test-*
|
| 40 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 38 |
- split: test
|
| 39 |
path: data/test-*
|
| 40 |
---
|
| 41 |
+
|
| 42 |
+
A triple professionally annotated ABSA dataset, specifically for Aspect Category Sentiment Analysis dataset which can be used for Aspect Category Detection (ACD) and Aspect Category Sentiment Classification (ACSC).
|
| 43 |
+
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. These categories are conceptual, i.e. they do not necessarily explicitly appear in the text, and come from a predefined list of Aspect Categories.
|
| 44 |
+
Aspect Category Sentiment Classification (ACSC) aims to classify the sentiment polarities of the conceptual aspect categories.
|
| 45 |
+
|
| 46 |
+
The predefined list of Aspect Categories for this dataset are:
|
| 47 |
+
```
|
| 48 |
+
category category_code
|
| 49 |
+
0 Account management: Account access account-management.account-access
|
| 50 |
+
1 Company brand: Competitor company-brand.competitor
|
| 51 |
+
2 Company brand: General satisfaction company-brand.general-satisfaction
|
| 52 |
+
3 Company brand: Reviews company-brand.reviews
|
| 53 |
+
4 Logistics rides: Speed logistics-rides.speed
|
| 54 |
+
5 Online experience: App website online-experience.app-website
|
| 55 |
+
6 Purchase booking experience: Ease of use purchase-booking-experience.ease-of-use
|
| 56 |
+
7 Staff support: Attitude of staff staff-support.attitude-of-staff
|
| 57 |
+
8 Staff support: Email staff-support.email
|
| 58 |
+
9 Staff support: Phone staff-support.phone
|
| 59 |
+
10 Value: Discounts promotions value.discounts-promotions
|
| 60 |
+
11 Value: Price value for money value.price-value-for-money
|
| 61 |
+
```
|
| 62 |
+
## Annotation
|
| 63 |
+
Two annotators have extensive experience in developing manually labelled ABSA datasets for a commercial company, but do not have a formal background/education related to linguistics. The third annotator has a PhD in computational linguistics and is assumed to be an expert tagger.
|