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With the development of social network。Dew words appear ceaselessly.The appearance of new word tends to characterize the social hot spot or represent certain public mood.The new word detection and emotional tendency judg— ment provide a new way for the public mood forecast.W e constructed the deep conditional random fields model for the sequence labeling,introduced part of speech,character position,the ability of word formation as features,and combined it with the crowd sourcing network dictionary and the other third party dictionary.Traditional method based on emo— tional dictionary is dificult to judge the new word emotional tendency.We expressed word as a vector of K dimension based on neural network language model in order to find the nearest words to the new word in the vector space Accord— ing to the emotional tendency of these words and the distance between them and the new word,the new word sentiment is judged.The experiment on corpus of Peking university demonstrates the feasibility of the proposed model and meth— od,in which the new word detection F-value iS 0.991,and the emotion recognition accuracy iS 7O .

基于深层结构模型的新词发现与情感倾向判定 (New Word Detection and Emotional Tendency Judgment Based on Deep Structured Model).