A text mining application of emotion classifications of Twitter's users using Naïve Bayes method

Abstract

Twitter is one of social media with more than 500 million users and 400 million tweets per day. In any written tweet of Twitter users it contains various emotions. Most research on the use of social media classifies sentiments into three categories that are positive, negative, and neutral. However, none of these studies has developed an application that can detect user emotions in the social media, particularly on Twitter. Hence, this research developed a text mining application to detect emotions of Twitter users that are classified into six emotions, namely happiness, sadness, anger, disgust, fear, and surprise. Three main phases of the text mining utilized in this application were preprocessing, processing, and validation. Activities conducted in the preprocessing phase were case folding, cleansing, stop-word removal, emoticons conversion, negation conversion, and tokenization to the training data and the test data based on the sentiment analysis that performed morphological analysis to build several models. In the processing phase, it performed weighting and classification using the Naive Bayes algorithm on the validated model. The process for measuring the level of accuracy generated by the application using 10-fold cross validation was done in the validation phase. The findings showed that this application is able to achieve 83% accuracy for 105 tweets. In order to get a higher accuracy, one requires a better model in training data.