Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies

Abstract

—Twitter has attracted the attention of many re-searchers owing to the fact that every tweet is, by default, public in nature which is not the case with Facebook. In this paper, we present sentiment analysis of tweets written in English, belonging to different telecommunication companies in Saudi Arabia. We apply different machine learning algorithms such as k nearest neighbor algorithm, Artificial Neural Networks (ANN), Nave Bayesian etc. We classified the tweets into positive, negative and neutral classes based on Euclidean distance as well as cosine similarity. Moreover, we also learned similarity matrices for kNN classification. CfsSubsetEvaluation as well as Information Gain was used for feature selection. The results of CfsSubsetEvaluation were better than the ones obtained with Information Gain. Moreover, kNN performed better than the other algorithms and gave 75.4%, 76.6% and 75.6% for Precision, Recall and F-measure, respectively. We were able to get an accuracy of 80.1% with a symmetric variant of kNN while using cosine similarity. Furthermore, interesting trends wrt days, months etc. were also discovered.