SVM based approach for opinion classification in Arabic written tweets

We propose a machine learning approach for automatically classifying opinions of Twitter texts written in Modern Standard Arabic (MSA). Tweets are classified as either positive, negative, neutral or non-opinion. Various features for opinion classification have been used which are mainly linguistic and numeric. Our in-house collected and developed training data consists of tweets preserving their specifications such as @usermentions, #hashtags which are used as tweet-particular features. Four machine learning algorithms were applied on our dataset: Support Vector Machine (SVM), Naive Bayes (NB), J48 decision tree and Random forest. The experiments results show that SVM gives the highest F measure (72%), while the j48 classifier gives the highest precision (70,97%). Our experimental results demonstrate that tweet's specific features can significantly improve classification performance in comparison to other features combination.