Classification and Regression Tree Method for Forecasting

Sentiment classification is a special task of text classification whose objective is to classify a text according to the sentimental polarities of opinions it contains e.g., favorable or unfavorable, positive or negative. This is especially a problem for the tweets sentiment analysis. Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. Twitter is an online social networking service that enables users to send and read short 140-charcters messages called “tweets”. Moreover, compared to product review, Twitter lacks data labeling and a rating mechanism to acquire sentiment labels. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. Twitter, attracts more people to post their feelings and opinions on various topics. The posting of sentiment contents cannot only give an emotional snapshot of the online but also have potential commercial, financial and sociological values. In social media, a Twitter user may have different opinions on different topics using a method called CART (classification and Regression Tree) method. CART analysis is a tree-building technique which is unlike traditional data analysis methods. Other factors which limit CART's general acceptability are the complexity of the analysis and, until recently, the software required to perform CART analysis was difficult to use.