تشخیص احساسات ناخوشایند کاربران با استفاده از رسانه های اجتماعی برای هوش تجاری Detecting users’ anomalous emotion using social media for business intelligence

1. Introduction According to the 2016 third quarter earnings report [1] released by Sina Weibo, as of September 30, 2016, the monthly number of active users on Weibo has reached 297 million. In September, 2016, the number of active users has reached 132 million, representing an increase of 32% over the same period last year. Micro-blog in the video, travel, sports and other fields have been further developed. In 2016, most ofthe active users on micro-blog are highly educated, they are the main force of micro-blog, accounting for up to 77.8%, and their emotional states are often characterized by the microblogs that they released. Sina Weibo has a large number of young users, and they are an important part of the main consumer and society. User emotion modeling and anomaly detection on micro-blog is an important field of emotional analysis, which can help the enterprises to make business decisions, help the government to monitor public opinion and public safety through social network, prevent the spread of irrational emotions in social network or even in real world, respond timely to the possible negative incidents to prevent some criminals who attempts to spread rumors [2] through micro-blog of high-precision emotional analysis system. Zhang [9] built an emotional dictionary based on the emotional words and phrases commonly used of emotional factors to recognize and classify the emotion on micro-blog, which achieved good results. Zhao [10] considered the object of a text to improve the emotional classifi- cation accuracy to detect the social anomaly, the Twitter text were chose as the sample for testing, by comparing the proportion of negative emotions to observe anomaly in a day, the conclusions are general and could not accurately analyze the specific abnormal event or user. Li [11], who was based on real-time event monitoring framework and system of micro-blog, proposed a rule-based and statistical method, used time series modelto monitor anomaly, which proved more effective than the ordinary model. Yin [12] proposed a micro-blog anomaly ranking detection method based on the lifting coefficient, which effectively prevented the artificial manipulation to improve the ranking of micro-blog. Experiments onthe simulationdata set showedthatthemethodcouldeffectively identify micro-blog anomaly ranking by micro-blog topology. Anomaly detection methods mentioned above are mainly based on dictionary, text, neural network, time series, statistic, rule and rank, which require a large number of annotated corpus, but the annotations workload are really heavy. In addition, the current methods tend to classify and analyze all the data on a social platform to detect outbreaks or abnormal events from the time aspect, but there is little research on the detection of abnormal emotion for the individual user.