Mansour-Essgaer/Libyan-Resturant

Libyan Restaurants: A Benchmark Dataset for Sentiment Analysis in the Libyan Arabic Dialect # Libyan Restaurants ### A Benchmark Dataset for Sentiment Analysis in the Libyan Arabic Dialect ## 📌 Overview The **Libyan Restaurants (LR)** is a manually annotated sentiment analysis dataset written in the Libyan Arabic dialect. The data was collected from real-world restaurant reviews on **Facebook** and **Google Maps**. The dataset is intended to serve as a benchmark resource for evaluating machine learning and deep learning models in **low-resource Arabic dialect NLP**. This repository provides the dataset, annotation details, and baseline results to ensure reproducibility and fair comparison across studies. --- ## 📊 Dataset Statistics | Attribute | Value | | :--- | :--- | | **Total comments** | **4,609** | | **Positive reviews** | 2,509 (55.4%) | | **Negative reviews** | 2,020 (44.6%) | | **Language** | Libyan Arabic dialect | | **Domain** | Restaurant reviews | | **Sources** | Facebook, Google Maps | | **Annotation** | Manual (3 annotators) | | **Labels** | Binary (1 = Positive, 0 = Negative) | --- ## 📁 Data Format The dataset is provided in **Excel format** with the following structure: * **`message`**: User comment written in Libyan Arabic. * **`label`**: Sentiment label (**1** = Positive, **0** = Negative). ### Example Data ```Excel message,label "المطعم باهي والخدمة سريعة",1 "الخدمة بلهون بكل والطلب تأخر",0 ``` --- ## 🧾 Annotation Process To ensure high linguistic validity and contextual accuracy, the following procedure was used: * **Filtration:** Comments were filtered to retain only Libyan dialect text. * **Labeling:** Two native Libyan annotators independently labeled each comment. * **Polarity:** Labels reflect overall sentiment polarity. * **Exclusions:** Neutral, spam, and non-opinion comments were excluded. --- ## 🧪 Benchmark Results (Baseline) Baseline experiments were conducted using **TF–IDF features** and classical machine learning models. These results are provided as reference baselines for future research. | Model | Accuracy …