Previous work on opinion mining and sentiment analysis mainly concerns product, movie, or literature reviews; few applied this technique to analyze the publicity of person. We present a novel document modeling method that utilizes embeddings of emotion keywords to perform reader's emotion classification, and calculates a publicity score that serves as a quantifiable measure for the publicity of a person of interest. Experiments are conducted on two Chinese corpora that in total consists of over forty thousand users' emotional response after reading news articles. Results demonstrate that the proposed method can outperform state-of-the-art reader-emotion classification methods, and provide a substantial ground for publicity score estimation for candidates of political elections. We believe it is a promising direction for mining the publicity of a person from online social and news media that can be useful for propaganda and other purposes.
|Title of host publication||The 4th International Workshop on Natural Language Processing for Social Media|
|Publication status||Published - Nov 11 2016|