dataset_info:
features:
- name: id
dtype: int64
- name: org_index
dtype: int64
- name: data_source
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- name: industry
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- name: text
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- name: labels
sequence:
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splits:
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num_examples: 7930
- name: validation
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- name: test
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num_examples: 1587
download_size: 1010316
dataset_size: 3466221
configs:
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data_files:
- split: train
path: data/train-*
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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, 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 or SentiHood).
This dataset is part of the FABSA paper, 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},
}