基于语义规则与RNN模型的在线评论情感分类研究

为提高互联网中在线评论文本的情感倾向分类准确率,方便消费者和商家准确高效地获取信息,该文提出一种将语义规则方法与深度学习方法相结合的在线评论文本情感分类模型,对基于情感词典的语义规则信息进行扩展,嵌入到常用特征模板中组合成更有效的混合特征模板;采用Fisher判别准则方法对混合特征模板进行降维以消除特征间的信息冗余;深度学习模型采用基于LSTM改进的RNN模型,将网络爬取的数据输入到模型进行训练和测试。结果表明,语义规则抽取出的特征包含更多、更准确的情感信息,使得混合特征模板可以更加全面地考虑文本的情感特征粒度;Fisher准则可有效识别出高判别性的低维文本特征,进一步提高改进RNN模型对评论文本的分类性能。

Semantic Rules and RNN Based Sentiment Classification for Online Reviews

In order to improve the sentiment classification of online reviews, a sentiment classification model based on semantic rules and deep learning model is proposed. We extend the semantic rule information based on sentiment dictionary, and embed it into common feature templates to form a more effective mixed feature template. And the Fisher discriminant criterion is applied to reduce the dimension of mixed feature templates to eliminate information redundancy. The deep learning model is based on a LSTM. The experiments on web crawled data show that the proposed method improves the classification for online reviews.