Sentiment Time Series Calibration for Event Detection

Event detection based on sentiment time series, which describe the trend of users' emotions or attitudes towards specific topics over time, has been widely applied in the analysis of social network or text mining. Most of the contributions directly generate time series sequences by classifiers. However, due to the missing corpus labels or the limited performance of the classifier, such generated sentiment time series may not correspond to the actual values, especially when the sentiment value changes drastically, called extreme value. We propose a new method to calibrate sentiment times series for event detection based on evaluation on a sampling dataset. Theoretical analysis of the calibration method is explicated, and it is proved that the sampling error of the performance indicators can be limited to a minimal range for extreme values, thus sentiment value error can be reduced. Experiments on simulated datasets and real-world datasets illustrate the effectiveness and robustness of our method.