Datasets:
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---
dataset_info:
features:
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dtype: int64
- name: org_index
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- name: data_source
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- name: test
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num_examples: 1587
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dataset_size: 3466221.0
configs:
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data_files:
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path: data/test-*
---
# FABSA, An aspect-based sentiment analysis dataset in the Customer Feedback space (Trustpilot, Google Play and Apple Store reviews).
A professionally annotated dataset released by [Chattermill AI](https://chattermill.com/), with 8 years of experience in leveraging advanced ML analytics in the customer feedback space for high-profile clients such as Amazon and Uber.
Two annotators possess extensive experience in developing human-labeled ABSA datasets for commercial companies, while the third annotator holds a PhD in computational linguistics.
## Task
This dataset encompasses **Aspect Category Sentiment Analysis** and is suitable for both **Aspect Category Detection** (ACD) and **Aspect Category Sentiment Classification** (ACSC). ACD in sentiment analysis identifies aspect categories mentioned in a sentence. These categories are conceptual; they may not explicitly appear in the review and are chosen from a predefined list of Aspect Categories. ACSC classifies the sentiment polarities of these 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 Scheme
The FABSA dataset is manually labeled according to a hierarchical annotation scheme that includes parent and child aspect categories. Each aspect category comes with an associated sentiment label (positive, negative, and neutral), creating a total of (12 × 3) target classification categories.
In line with prior studies, we employ a multi-label classification scheme where each review is tagged with one or more aspect + sentiment labels. Thus, a single review can cover multiple aspects and express various (sometimes contrasting) polarities. For example,
```
Customer Review:
“product is very good but customer service is really bad, they never respond”
Labels: [Company brand: General satisfaction, positive), (Staff support: Attitude of staff, Negative)"
```
## Release
There has been a lack of high-quality ABSA datasets covering broad domains and addressing real-world applications. Academic progress has been confined to benchmarking on domain-specific, toy datasets such as restaurants and laptops, which are limited in size (e.g., [SemEval Task ABSA](https://aclanthology.org/S16-1002.pdf) or [SentiHood](https://aclanthology.org/C16-1146/)).
This dataset is part of the [FABSA paper](https://www.sciencedirect.com/science/article/pii/S0925231223009906), and we release it hoping to advance academic progress as tools for ingesting and analyzing customer feedback at scale improve significantly, yet evaluation datasets continue to lag. FABSA is a new, large-scale, multi-domain ABSA dataset of feedback reviews, consisting of approximately 10,500 reviews spanning 10 domains (Fashion, Consulting, Travel Booking, Ride-hailing, Banking, Trading, Streaming, Price Comparison, Information Technology, and Groceries).
## Citation
```
@article{KONTONATSIOS2023126867,
title = {FABSA: An aspect-based sentiment analysis dataset of user reviews},
journal = {Neurocomputing},
volume = {562},
pages = {126867},
year = {2023},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2023.126867},
url = {https://www.sciencedirect.com/science/article/pii/S0925231223009906},
author = {Georgios Kontonatsios and Jordan Clive and Georgia Harrison and Thomas Metcalfe and Patrycja Sliwiak and Hassan Tahir and Aji Ghose},
keywords = {ABSA, Multi-domain dataset, Deep learning},
}
``` |