Experiment of Sentiment Analysis on Public Comments of Hot Topics

With the popularity of the Internet, the public can get news from the recent hottest news, and express their opinion in time in the network of social media, such as microblog, twitter. With the help of sentiment analysis of the comments, the government or the media can inform of the public opinion, and make corresponding decisions, so that they are able to get the positive feedback. Also, the sentiment analysis can help people block the detrimental comments. Since the sentiment analysis is useful in the daily life, the author made an experiment about the sentiment analysis of three models, namely, Naive Byes, Maximum Entropy, and SVM, to compare the results' accuracy of them. And the dataset used in this experiment is Stanford Twitter Sentiment (STS). Besides, the reference is made to Stanford Sentiment Treebank and IMDB. In addition, the determination of the emotional tone is based on emotional dictionaries like GI (General Inquirer) and How Net. By comparing the accuracy and training time of different models, SVM is selected to be the optimal model with the F-scores of 82%.