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README.md

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-license: cc-by-sa-4.0
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+# TRABL: Travel-Domain Aspect-Based Sentiment Analysis Dataset
+
+This repository contains the **TRABL dataset**, released in support of our paper accepted to **The ACM Web Conference 2026 (WWW 2026)**:
+
+> **TRABL: A Unified Framework for Travel Domain Aspect-Based Sentiment Analysis Applications of Large Language Models**
+
+The dataset is designed to support research on **Aspect-Based Sentiment Analysis (ABSA)** in the travel domain, with a particular focus on *joint extraction* of structured sentiment tuples and *supporting evidence snippets*.
+
+---
+
+## 1. Overview
+
+TRABL extends the standard ABSA *quad prediction* task by additionally requiring the extraction of **textual evidence snippets** that justify each extracted sentiment tuple. The dataset enables training and evaluation of models for both fine-grained opinion mining and downstream travel applications such as recommendation, review summarization, and property type detection.
+
+The data consists of **English user reviews** from three travel-related domains:
+
+* **Hotels**
+* **Attractions**
+* **Destinations**
+
+Each review is annotated by **two independent annotators**, following carefully designed guidelines and quality control procedures.
+
+---
+
+## 2. Task Definition
+
+Given a review text (T), the task is to extract a set of tuples of the form:
+
+```
+(aspect_term, aspect_category, opinion_span, sentiment, snippet)
+```
+
+Where:
+
+* **Aspect Term**: Explicit mention of the target entity (e.g., *"room"*)
+* **Aspect Category**: Semantic category from a **closed set of 112 travel-domain categories**
+* **Opinion Span**: Evaluative expression (e.g., *"spacious"*)
+* **Sentiment**: One of `{positive, negative, neutral}`
+* **Snippet**: A *contiguous span* of text from the review that provides sufficient evidence for the other fields
+
+Some fields may be `null` if not explicitly mentioned (e.g., implicit aspects).
+
+This formulation generalizes multiple ABSA subtasks, including:
+
+* Category extraction `(c)`
+* Category–sentiment extraction `(c, p)`
+* Category–sentiment with evidence `(c, p, s)`
+* Standard quad prediction `(a, o, c, p)`
+* Quad prediction with snippet extraction `(a, o, c, p, s)`
+
+---
+
+## 3. Dataset Splits
+
+The dataset is split into **train**, **validation**, and **test** sets as follows:
+
+| Split      | #Rows |
+| ---------- | ----- |
+| Train      | 5,768 |
+| Validation | 898   |
+| Test       | 900   |
+
+> Note: A *row* corresponds to a single annotated review instance (including annotator-specific annotations).
+
+---
+
+## 4. Data Format
+
+The data is released in **JSONL** format (one JSON object per line).
+
+Each example contains the following top-level fields:
+
+* **review_id**: Unique identifier for the review
+* **annotator**: Identifier of the annotator (`1` or `2`)
+* **text**: Full review text
+
+  * Hotel reviews include title, positive, and negative sections
+  * Attraction and destination reviews contain free text only
+* **labels**: List of labeled tuples
+
+Each entry in `annotations` has the structure:
+
+```json
+{
+  "aspect_term": "value for money",
+  "aspect_category": "value for money",
+  "opinion_span": "good",
+  "sentiment": "positive",
+  "snippet": "The good value for money"
+}
+```
+
+---
+
+## 5. Example
+
+**Input Review (excerpt):**
+
+```
+The good value for money, the helpful & polite staff, the level of cleanliness.
+```
+
+**Annotations (excerpt):**
+
+```json
+[
+  {
+    "aspect_term": "value for money",
+    "aspect_category": "value for money",
+    "opinion_span": "good",
+    "sentiment": "positive",
+    "snippet": "The good value for money"
+  },
+  {
+    "aspect_term": "staff",
+    "aspect_category": "property staff and service support",
+    "opinion_span": "helpful & polite",
+    "sentiment": "positive",
+    "snippet": "the helpful & polite staff"
+  }
+]
+```
+
+---
+
+## 6. Annotation Process and Quality
+
+* Each review was annotated by **two independent annotators**
+* Annotation was supported by LLM-generated initial labels, followed by human correction
+* Disagreements were normalized using post-processing rules
+* **Overall annotator agreement** (F1): ~67%, consistent with prior ABSA benchmarks
+* Agreement exceeds **80%** when evaluated on partial field subsets (e.g., category or sentiment only)
+
+---
+
+## 7. Statistics
+
+* **Domains**: Hotels, Attractions, Destinations
+* **Total number of reviews**: 3,783
+* **Average review length**: ~42 words
+* **Average number of extracted tuples per review**:
+
+  * Annotator 1: ~6.96
+  * Annotator 2: ~6.77
+* **Sentiment distribution**: Skewed positive, reflecting real-world travel review behavior
+
+---
+
+## 8. Intended Use
+
+This dataset is intended for **research purposes only**, including but not limited to:
+
+* Aspect-Based Sentiment Analysis
+* Opinion mining and evidence extraction
+* Travel-domain NLP
+* Evaluation of LLMs and lightweight fine-tuned models
+* Structured information extraction with explainability
+
+---
+
+## 9. Citation
+
+If you use this dataset, please cite:
+
+```
+@inproceedings{madmon2026trabl,
+  title     = {TRABL: A Unified Framework for Travel Domain Aspect-Based Sentiment Analysis Applications of Large Language Models},
+  author    = {Madmon, Omer and Golan, Shiran and Fainman, Eran and Kleinfeld, Ofri and Beladev, Moran},
+  booktitle = {Proceedings of The ACM Web Conference},
+  year      = {2026}
+}
+```
+
+---
+
+## 10. License and Privacy
+
+* All reviews were processed under strict internal privacy and PII guidelines
+* The dataset is released for **non-commercial research use**, cc-by-sa-4.0 license
+* No personally identifiable information is included
+
+---
+
+For questions or issues related to the dataset, please refer to the paper or open an issue on the dataset repository.