Arabic Language Sentiment Analysis on Health Services

TL;DR

This paper presents a new Arabic sentiment analysis dataset focused on health services from Twitter, and evaluates various machine learning and deep learning models on this dataset.

Contribution

It introduces a large, annotated Arabic health-related sentiment dataset and compares multiple ML and deep learning approaches for sentiment classification.

Findings

Deep learning models outperform traditional ML algorithms

Support Vector Machine achieves high accuracy

Dataset provides a valuable resource for Arabic sentiment analysis

Abstract

The social media network phenomenon leads to a massive amount of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.