【摘要】 目前基于词嵌入的卷积神经网络文本分类方法已经在情感分析研究中取得了很好的效果。此类方法主要使用基于上下文的词嵌入特征,但在词嵌入过程中通常并未考虑词语本身的情感极性,同时此类方法往往缺乏对大量人工构建情感词典等资源的有效利用。针对这些问题,该文提出了一种结合情感词典和卷积神经网络的情感分类方法,利用情感词典中的词条对文本中的词语进行抽象表示,在此基础上利用卷积神经网络提取抽象词语的序列特征,并用于情感极性分类。该文提出的相关方法在中文倾向性分析评测COAE2014数据集上取得了比目前主流的卷积神经网络以及朴素贝叶斯支持向量机更好的性能。更多还原

【Abstract】 Recently,the classification approach based on word embedding and convolutional neural networks achieved good results in sentiment classification task.This approach is mainly based on the contextual features of the word embeddings without the consideration of the polarity of the words.Meanwhile,this approach lacks of the use of manually compiled sentiment lexicon resources.To address these problems,this paper proposes a novel sentiment classification method which incorporates existing sentiment lexicon and convolutional neural networks.In this work,the words in text are abstractly represented by using existing sentiment words.The convolutional neural networks are used to extract sequence features from the abstracted word embeddings.Finally,the sequence features are applied to sentiment classification.The evaluations on Chinese Opinion Analysis Evaluation 2014 dataset show that our proposed approach outperforms the convolutional neural networks model with word embedding features and Nave Bayes Support Vector Machines.更多还原