Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are numerous web sites built on the Internet presenting Twitter. The user can enter a sentiment target and seek for tweets containing positive, negative, or neutral opinions. This is remarkable for consumers to investigate the products before purchase automatically.
Methods: This paper suggests a model for sentiment classification. The goal of this model is to investigate what is the role of n-grams and sampling techniques in Sentiment Classification application using an ensemble method on Twitter datasets. Also, it examines both binary and multiple classifications, which are classified datasets into positive, negative, or neutral classes.
Results: Twitter Classification is an outstanding problem, which has very few free resources and not available due to modified authorization status. However, all Twitter datasets are not labeled and free, except for our applied dataset. We reveal that the combination of ensemble methods, sampling techniques, and n-grams can improve the accuracy of Twitter Sentiment Classification.
Conclusion: The results confirmed the superiority of the proposed model over state-of-the-art systems. The highest results obtained in terms of accuracy, precision, recall, and f-measure.