Sentiment analysis algorithms: evaluation performance of the Arabic and English language

Usage of social media like Facebook, WhatsApp, Twitter, and Blogs is rapidly increasing in recent years. These platforms allow people to freely write comments and share their opinions, ideas and suggestions that can be either positive, negative or neutral comments on various topics such as politics, business, advertisement, and entertainment. Several, Machine Learning (ML) algorithms such as Naïve Bayes NB and Decision Tree DT are used with sentiments analysis technique in different languages to understand the opinions of people in social media. In this paper, we evaluate and discussed the application of NB and DT in sentiment analysis using a multi-dataset in different languages to understand which can give a better result when used with ML algorithms. Multi-language dataset such as English, modern standard Arabic and dialect Arabic are collected for the experiment. We evaluate is based on two parameters which are accuracy and runtime. The result of our experiment shows some significant.