---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: org_index
    dtype: int64
  - name: data_source
    dtype: string
  - name: industry
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      sequence: string
  - name: label_codes
    dtype: string
  splits:
  - name: train
    num_bytes: 2599501.8469831664
    num_examples: 7930
  - name: validation
    num_bytes: 346490.977586533
    num_examples: 1057
  - name: test
    num_bytes: 520228.17543030076
    num_examples: 1587
  download_size: 1010316
  dataset_size: 3466221.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

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).
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.
Aspect Category Sentiment Classification (ACSC) aims to classify the sentiment polarities of the conceptual aspect categories.

The predefined list of Aspect Categories for this dataset are:
```
                                    category                            category_code
0         Account management: Account access        account-management.account-access
1                  Company brand: Competitor                 company-brand.competitor
2        Company brand: General satisfaction       company-brand.general-satisfaction
3                     Company brand: Reviews                    company-brand.reviews
4                     Logistics rides: Speed                    logistics-rides.speed
5             Online experience: App website            online-experience.app-website
6   Purchase booking experience: Ease of use  purchase-booking-experience.ease-of-use
7           Staff support: Attitude of staff          staff-support.attitude-of-staff
8                       Staff support: Email                      staff-support.email
9                       Staff support: Phone                      staff-support.phone
10               Value: Discounts promotions               value.discounts-promotions
11              Value: Price value for money              value.price-value-for-money
```
## Annotation
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.