LASTD: A Manually Annotated and Tested Large Arabic Sentiment Tweets Dataset

Abstract

With the growing attention towards Arabic Sentiment Analysis (SA), the availability of annotated dataset has raised. Although acquiring dataset from social media platforms, microblogs and so on is an easy task, annotation is the hard part. Dataset annotation requires a lot of manual tedious work which stands as a major problem. In addition to that, some datasets are built in house and aren't available for public access. This paper introduces the LASTD which is a manually annotated dataset for Arabic tweets sentiment analysis along with an insight of its statistics and benchmarks. It consists of more than 15K Arabic tweets annotated as positive, negative and neutral. Using 10-cross validation, three different classifiers were trained and tested for 3-class classification problem and 2-class classification problem. The support vector machine (SVM) classifier tends to have the highest accuracy. LASTD is made public for academic research.