Trend prediction of hot words in weibo based on fuzzy time series
Hot words have been widely applied to reflect social opinion in microblogs. Many researchers introduce hard computing techniques to evaluate different trends of hot words, but these methods may result in biased prediction due to inaccurate classification. In this paper, the temporal patterns of hot words are introduced based on fuzzy time series theory, which uses the soft computing method to improve accuracy of classification. Fuzzy membership functions are applied to determine fuzzy sets, which increase flexibility and diversity of time series classification schemes. Based on fuzzy inference rules, specific trends of time series values are schemed to enhance accuracy of time series prediction. Compared with traditional method of time series prediction, experimental results show that our algorithm performs well as for capturing the rise and fall trends and achieving higher accuracy of time series prediction of hot words in microblog.
2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)