Hybrid Model Based Influenza Detection with Sentiment Analysis from Social Networks

Abstract

Sina microblog is a popular microblogging service in China, which could provide perfect reference sources for flu detection due to its’ real-time characteristic and large number of active users posting about their daily life continually. In this paper, we investigate the real-time flu detection problem and propose a flu detection model with emotion factors(sentiment analysis) and sematic information (Em-Flu model). First, we extract flu-related microblog posts automatically in real-time using a trained SVM filter. For posts classification, we also adopt association rule mining to extract strongly associated features as additional features of posts to overcome the limitation of 140 words, including sentiment analysis information which can help to classify the posts without explicit flu-related features. Then Conditional Random Field model is revised and applied to detect the transition time of flu that we can find out which place is more likely for influenza outbreak and when is more likely for influenza outbreak in one city or a province in China. Experimental results on detecting flu situation during certain time in some locations show the robustness and effectiveness of the proposed model.