Detecting Tweet-Based Sentiment Polarity of Plastic Surgery Treatment

Sentiment analysis is a growing research these days. Many companies perform this analysis on public opinions to get a general idea about any product or service. This paper presents a novel approach to get views or comments of Twitter users about plastic surgery treatments. The proposed approach uses machine-learning technique embedded with the naïve Bayesian classifier to assign polarities (i.e. positive, negative or neutral) to the tweets, collected from ?Twitter micro-blogging website?. The accuracy of the obtained results has been validated using precision, recall and F-score measures. It has been observed from 25000 tweets dataset that people tend to have positive as well as substantial negative opinions regarding particular treatments. The experimental results show the effectiveness of the proposed approach.