Sentiment Analysis of Snapchat Application's Reviews

Sentiment analysis is a process of extracting opinion and subjectivity knowledge from user generated text content without the need to monitor the reviews manually. It can help to obtain an overview on performance of a product or subject based on the reviews from users. The aim of this study is to classify the Snapchat application's reviews into different polarities which are positive, neutral or negative. Next, the most frequent words are identified. Furthermore, Multinomial Naïve Bayes and Random Forest classification algorithm are used to predict the user's rating. The performances of the classification models are evaluated using accuracy, precision, recall and F1-score. The results showed majority of the Snapchat users had a positive experience with the total of 6037 positive reviews. Based on the performance measures, Multinomial Naïve Bayes classification algorithm performed slightly better than the Random Forest classification algorithm in predicting the rating of Snapchat application. Overall, both of the classification algorithms have average performance in predicting user's rating for Snapchat application.