Update README.md

README.md

@@ -39,15 +39,24 @@ configs:
     path: data/test-*
 ---
 
-However, the development of such manually curated 
+## 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.
 
-datasets is a labour-intensive process and therefore existing ABSA datasets cover only a few domains and they are limited in size. In response, we present FABSA (Feedback ABSA), a new large-scale and multi-domain ABSA dataset of feedback reviews. FABSA consists of approximately 10,500 reviews which span across 10 domains
+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).
+
+
+
+## Task
+
+This dataset covers **Aspect Category Sentiment Analysis** and can be used for both **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 review and are selected from a predefined list of Aspect Categories.
+**Aspect Category Sentiment Classification** (ACSC) aims to classify the sentiment polarities of the above conceptual aspect categories.
 
-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:
 ```
@@ -65,19 +74,33 @@ The predefined list of Aspect Categories for this dataset are:
 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.
-
 
 ## Annotation Scheme
 
-3.2. Annotation scheme
+The FABSA dataset is manually labelled against a hierarchical annotation scheme which consists of parent and child aspect categories. Each aspect category is associated with a sentiment label (positive, negative and neutral). This creates a total of (12 × 3) target classification categories.
 
-The FABSA dataset is manually labelled against a hierarchical annotation scheme which consists of
-parent and child aspect categories (Fig. 2). Each aspect category is associated with a sentiment label (positive, negative and neutral). This creates a total of
+Following previous work, we adopt a multi-label classification scheme wherein each review is labelled with one or more aspect ＋ sentiment label. Accordingly, a single review may contain multiple different aspects and express different (and in some cases 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)"
 
-(12 × 3) target classification categories.
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/608995d619137b3a6ba76113/Lr60Oc6NGalkOEj-_Gtzk.jpeg)
 
-Following previous work, we adopt a multi-label classification scheme wherein each review is labelled with one or more aspect＋ sentiment label. Accordingly, a single review may contain multiple different aspects and express different (and in some cases contrasting) polarities. Table 3 shows an example of a review which is associated with two different aspect categories and two different and conflicting polarity labels (positive and negative, respectively).
 
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/608995d619137b3a6ba76113/Lr60Oc6NGalkOEj-_Gtzk.jpeg)
+## 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},
+abstract = {Aspect-based sentiment analysis (ABSA) aims at automatically extracting aspects of entities and classifying the polarity of each extracted aspect. The majority of available ABSA systems heavily rely on manually annotated datasets to train supervised machine learning models. However, the development of such manually curated datasets is a labour-intensive process and therefore existing ABSA datasets cover only a few domains and they are limited in size. In response, we present FABSA (Feedback ABSA), a new large-scale and multi-domain ABSA dataset of feedback reviews. FABSA consists of approximately 10,500 reviews which span across 10 domains. We conduct a number of experiments to evaluate the performance of state-of-the-art deep learning models when applied to the FABSA dataset. Our results demonstrate that ABSA models can generalise across different domains when trained on our FABSA dataset while the performance of the models is enhanced when using a larger training dataset. Our FABSA dataset is publicly available.11https://github.com/kontonag86/fabsa-dataset.}
+}
+```