This repository is based on a research article on sentiment analysis of Lebanese Arabic costumer reviews. It proposes a logistic regression approach paired with term and inverse document frequency (TF*IDF) for Arabic sentiment classification on services’ reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. We collected manually from Google reviews and Zomato, which have reached to 3916 reviews. Experiments show three core findings: 1) The classifier is confident when used to predict positive reviews. 2) The model is biased on predicting reviews with negative sentiment. Finally, the low percentage of negative reviews in the corpus contributes to the diffidence of logistic regression model.
Cite as: Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A. (2019). Sentiment Classifier: Logistic Regression for Arabic Services’ Reviews in Lebanon. 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp. 1-5. Doi: 10.1109/ICCISci.2019.8716394
Note: OCLAR Dataset is available in the implementation folder as well as in the following link: https://huggingface.co/datasets/oclar
For more information: contact me on marwanalomari(at)(dot)yahoo(dot)com