دانلود رایگان مقاله انگلیسی تشخیص احساسات ناخوشایند کاربران با استفاده از رسانه های اجتماعی برای هوش تجاری - الزویر 2018

abstract

Anomaly detection in sentiment analysis refers to detecting users’ abnormal opinions, sentiment patterns or special temporal aspects of such patterns. Users’ emotional state extracted from social media contains business information and business value for decision making. Social media platforms, such as Sina Weibo or Twitter, provide a vast source of information, which include user feedbacks, opinions and information on most issues. Many organizations also leverage social media platforms to publish information about events, products, services, policies and other topics frequently, analyzing social media data to identify abnormal events and make decisions in a timely manner is a beneficial topic. This paper adopts the multivariate Gauss distribution with the power-law distribution to model and analyze the users’ emotion of micro-blogs and detect abnormal emotion state. With the measure of joint probability density value and the validation of the corpus, anomaly detection accuracy of individual user is 83.49% and of different month is 87.84% by this method. Through the distribution test, the results show that individual users’ neutral, happy and sad emotions obey the normal distribution, but the surprised and angry emotions do not. Besides, emotions of micro-blogs released by groups obey power-law distribution, but the individual emotions do not. This paper proposes a quantitative method for abnormal emotion detection on social media, which automatically captures the correlation between different features of the emotions, and saves a certain amount of time by batch calculation of the joint probability density of data sets. The method can help the businesses and government organizations to make decisions according to the user’s affective disposition, intervene early or adopt proper strategies if needed.

6. Conclusion

This paper makes a study on the users emotion modeling and abnormal emotion detection on social media. The multivariate Gaussian model and joint probability density are introduced to detect abnormal users emotions of user on micro-blog. Results show that the accuracies of abnormal detection are 83.49% and 87.84% according to the user and month respectively. The experiment also shows that the “neutral, happy, sad” emotions of the individual user are subject to the normal distribution through the K-S test, while the “surprised, angry” emotions are not, and the emotion of micro-blogs released by group is subject to power-law distribution, while the individual user is not. This paper combines multivariate Gaussian model with joint probability density to detect anomalies on social media, and proposes a relatively comprehensive approach to model user and group emotions, which are meaningful to detect the abnormal emotion, monitor the public security and help the enterprise to make the reasonable business decision.