---
title: 'EHSAN: Hybrid ABSA Pipeline in Healthcare'
url: https://www.emergentmind.com/topics/ehsan
type: topic
---

# EHSAN: Hybrid ABSA Pipeline in Healthcare

EHSAN is a hybrid data-centric pipeline for Arabic aspect-based sentiment analysis (ABSA) in healthcare settings. It addresses the challenges posed by dialect variation, limited annotated resources, and lack of explainability in automated sentiment analysis systems for Arabic-language patient feedback. The framework systematically integrates ChatGPT-generated pseudo-labelling, targeted human review, and transformer-based modeling, culminating in the first explainable Arabic ABSA dataset with per-sample rationales for healthcare themes [2508.02574].

## 1. Hybrid Pseudo-Labelling and Review Pipeline

EHSAN employs a staged, “pseudo-label plus review” workflow to construct a robust annotated corpus:

- **Data Collection and Pre-processing:** Patient reviews (n=5,000) were scraped from three Saudi hospitals (Riyadh, Jeddah, Dammam). Texts were normalized via regex-based sentence segmentation and the AraBERT preprocessor to ensure homogeneity of Arabic script and remove noise (diacritics, emojis, elongations, extraneous whitespace).

- **Topic-Constrained Sentence Segmentation:** GPT-4-mini, via prompt engineering, split each review into semantically coherent, single-topic sentences. Coverage was validated using BERTopic (yielding ≈25 data-driven topics) and compared with Stanza’s rule-based segmentation; ChatGPT splits were adopted, resulting in 6,000 sentences.

- **ChatGPT Pseudo-Labelling:** Each sentence was processed with a few-shot prompt to assign (1) an aspect label from a 17-class taxonomy, (2) sentiment ∈ {Positive, Negative, Neutral}, and (3) a free-text rationale (initially in English, then translated to Arabic). Sentence-level IDs enabled traceability from raw data to annotation.

- **Tiered Human Verification:** Two native-Arabic annotators with specialized training on the taxonomy reviewed labels. Inter-annotator reliability yielded Cohen’s κ = 0.873 on a 5% subsample. Three dataset variants were produced by varying the proportion of human-reviewed labels:
    - Fully Supervised (FSD): 100% human-reviewed
    - Semi-Supervised (SSD): 50% human-reviewed
    - Unsupervised (USD): 0% human review (purely ChatGPT labels)

- **Final Dataset Splitting and Label Schemes:** Sentences were partitioned into train (3,583), dev (1,189), and test (1,190) subsets. Aspect labels were provided both in the original 17-category granularity and a merged 6-class taxonomy, facilitating analysis of granularity effects.

## 2. Annotation Schema and Rationales

Each sentence in EHSAN is annotated with two categorical fields:

- **Aspect:** Either one of 17 fine-grained healthcare-related classes (e.g., Medical_and_Nursing_Staff, Billing_and_Finance, Privacy) or one of 6 coarser-level groupings.
- **Sentiment:** One of {Positive, Negative, Neutral}.

A distinctive feature is the inclusion of model-generated rationales—free-text justifications for each (aspect, sentiment) assignment. These rationales, supplied by ChatGPT and translated into Arabic when needed, serve to enhance transparency and facilitate post-hoc auditing of annotation decisions.

## 3. Dataset Construction and Labeling Variants

The resulting dataset comprises 6,000 sentences, each mapped to an aspect and sentiment, with distinct subsets:

| Subset   | Size  | Label Verification        | Notes                       |
|----------|-------|--------------------------|-----------------------------|
| Train    | 3,583 | FSD, SSD, USD variants   | Human review ratio varies   |
| Dev      | 1,189 | 100% human-verified      | Consistent validation set   |
| Test     | 1,190 | 100% human-verified      | Consistent evaluation set   |

This design enables controlled experiments on annotation quality and its downstream impact. The 17→6 class grouping also supports direct analysis of granularity effects on model robustness.

## 4. Transformer-Based Aspect and Sentiment Classification

Two transformer architectures were fine-tuned for both aspect classification and sentiment analysis:

- **AraBERT v0.2 (Large):** Pre-trained on Modern Standard and dialectal Arabic, employing a custom tokenizer optimized for Arabic morphology.
- **DistilBERT (Multilingual):** A 6-layer, distilled BERT supporting over 100 languages, using a multilingual WordPiece tokenizer.

Key training settings included batch size 4, learning rate $10^{-5}$, 5 epochs, input sequence length up to 128 tokens, AdamW optimizer, and early stopping on dev macro-F1. Experiments were run for (a) 17-class aspects, (b) 6-class aspects, and (c) 3-class sentiment tasks, using PyTorch and HuggingFace infrastructure.

## 5. Evaluation Protocols and Performance Analysis

Evaluation metrics prioritize class-imbalance robustness:

- **Macro-Precision, Macro-Recall, Macro-F1:** Computed by averaging class-wise scores equally across all classes $K$:
  $$
  \text{Macro-Precision} = \frac{1}{K} \sum_{n=1}^K \text{Precision}_n
  $$
  $$
  \text{Macro-Recall} = \frac{1}{K} \sum_{n=1}^K \text{Recall}_n
  $$
  $$
  \text{Macro-F1} = \frac{2 \cdot \text{Macro-Precision} \cdot \text{Macro-Recall}}{\text{Macro-Precision} + \text{Macro-Recall}}
  $$
- **Overall Accuracy** is also reported.

### 17-Class Aspect Classification

- AraBERT achieved macro-F1 = 0.66 (FSD), with only a ΔF1 = 0.02 drop (to 0.64) for USD. DistilBERT trailed by 0.05–0.07 points.
- Cohen's κ for annotators was 0.873, indicating strong reliability.
- Statistical significance for FSD vs. USD for AraBERT: $p = 0.021$; all other differences not significant.

### 6-Class Aspect Classification

- AraBERT FSD macro-F1 increased to 0.78; human-review impact (FSD vs. USD) diminished to ΔF1 = 0.01.
- DistilBERT achieved macro-F1 = 0.69 (FSD).
- The reduction in aspect granularity yields F1 gains of +0.08 to +0.13, depending on the model.

### Sentiment Classification

- AraBERT exhibited macro-F1 = 0.84 (both FSD and USD), with accuracy 0.92–0.93.
- DistilBERT consistently scored macro-F1 ≈ 0.67–0.67, trailing AraBERT by ≈0.17.

## 6. Patterns, Granularity Effects, and Interpretability

Reducing aspect taxonomy from 17 to 6 categories significantly improved all metrics (AraBERT: F1 from 0.66→0.78, USD 0.64→0.77; DistilBERT similarly benefited). This supports the proposition that coarser classes lower sample complexity and attenuate label noise.

A notable finding is the robustness of ChatGPT-generated labels: the absence of human review led to only marginal F1 loss (≤0.02) in the most challenging aspect setting, and virtually none for 6-class and sentiment tasks.

Model-generated rationales improved annotator trust and provide an operational basis for explainable ABSA, offering justifications that can be systematically audited.

## 7. Prospects and Limitations

Key directions cited for extending EHSAN include:

- Generalization across hospitals, Arabic dialects, and healthcare regions.
- Further prompt engineering to minimize residual LLM labelling errors and bias.
- Multi-task models enabling simultaneous label prediction and natural language rationale generation.
- Auditing rationales for fairness across dialects and patient groups.
- Dynamic taxonomy adaptation and provenance tracking for full traceability of annotation decisions.
- Integration of privacy-preserving similarity search for sensitive patient feedback.

*This suggests* that the hybrid pseudo-labelling paradigm provides an effective template for building explainable, data-driven ABSA resources in low-resource and domain-sensitive contexts [2508.02574].

Source: https://www.emergentmind.com/topics/ehsan