System and method for predicting early development tendency of hot topics

Abstract

The invention provides a system and method for predicting early development tendency of hot topics. The method includes the steps of collecting a topical time series; judging whether the series entersa recession period; if the sequence enters the recession period, adopting a clustering method as the complete topical time series to perform classification to obtain various topic classes, and substituting each topical time series of each topic class into a prediction model to perform training to obtain an intra-class prediction model for each topic class; if the sequence does not enter the recession period, analyzing similarity between a new topic time series and each complete topical time series in the topic classes, and taking average values as the matching degrees of new topics and the topic classes; screening out a set number of topic classes in descending order of the matching degrees; calling the intra-class prediction model of the topic classes screened out, and substituting the intra-class prediction model into the new topic time series to obtain a set number of predicted values; assigning different weight values to the predicted values to perform combination to obtain predicted values of new topics in the future time. According to the system and method, the early development tendency of a hot topic can be predicted accurately.

Description

technical field [0001] The present invention relates to the field of public opinion analysis, and more specifically, to a hot topic early development trend prediction system and prediction method. Background technique [0002] Hot topics are often characterized by unpredictable, large impact, complex situations, high sensitivity, and serious consequences. Improper handling of them will cause many adverse social reactions and affect social stability. Since most of the existing hot topic development trend prediction algorithms predict its future development based on the existing development trend of the topic itself, it is impossible to predict its popularity and change trend when the topic first appears. Although the long-term prediction of topics has important application value, short-term predictions, especially for new topics, are more meaningful due to the short-term outbreak cycle of Internet public opinion. As a matter of fact, it takes only a few days for many public ...

AI Technical Summary

Problems solved by technology

As we all know, the development of topics generally fluctuates greatly in the early stage and is relatively stable in the later stage. Testing the prediction model based on relatively stable data, the scalability and universality of the model cannot be guaranteed

Moreover, the prediction model based on the previous data training can only accurately predict the development trend in the middle and late stages, and cannot predict the popularity and change trend of the topic when it first appears, which loses the timeliness of the model

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