Update README.md

README.md

@@ -39,6 +39,12 @@ configs:
     path: data/test-*
 ---
 
+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
+
 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.
@@ -61,3 +67,17 @@ The predefined list of Aspect Categories for this dataset are:
 ```
 ## 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 (Fig. 2). Each aspect category is associated with a sentiment label (positive, negative and neutral). This creates a total of
+
+(12 × 3) target classification categories.
+
+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)