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

随着社交网络的发展,新的词汇不断出现。新词的出现往往表征了一定的社会热点,同时也代表了一定的公众情绪,新词的识别与情感倾向判定为公众情绪预测提供了一种新的思路。通过构建深层条件随机场模型进行序列标记,引入词性、单字位置和构词能力等特征,结合众包网络词典等第三方词典。传统的基于情感词典的方法难以对新词情感进行判定,基于神经网络的语言模型将单词表示为一个K维的词义向量,通过寻找新词词义向量空间中距离该新词最近的词,根据这些词的情感倾向以及与新词的词义距离,判断新词的情感倾向。通过在北京大学语料上的新词发现和情感倾向判定实验,验证了所提模型及方法的有效性,其中新词判断的F值为0.991,情感识别准确率为70%。

With the development of social network, new 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. We 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 difficult 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 70%.