A Method for Network Topic Attention Forecast Based on Feature Words

Abstract

The number of people who obtain information and express ideas via the Internet is increasing rapidly. Research on identifying how much attention paid to a given online topic plays an important role in the field of public opinion management. We propose a method to predict the netizens' attention on a specific online topic in this paper. Firstly, we acquire the historical topics' attention-degrees by analyzing news, reviews and forum posts, then built up the Feature Words Set (FWS) and estimate the popularity of each feature word. After that, we extract the feature words from a new topic and evaluate their contribution to it. Finally, the new attention-degree is computed by comparing the new topic's feature words with those in FWS. We compare our method with the Support Vector Regression model on a data set of manually selected topics. Experimental results show that our approach is acceptable for predicting the attention-degree of online topics.