基于LSTM的商品评论情感分析

随着电子商务的发展,产生了大量的商品评论文本.针对商品评论的短文本特征,基于情感词典的情感分类方法需要大量依赖于情感数据库资源,而机器学习的方法又需要进行复杂的人工设计特征和提取特征过程.本文提出采用长短期记忆网络（Long Short-Term Memory）文本分类算法进行情感倾向分析,首先利用Word2vec和分词技术将评论短文本文本处理为计算机可理解的词向量传入LSTM网络并加入Dropout算法以防止过拟合得出最终的分类模型.实验表明：在基于深度学习的商品评论情感倾向分析中,利用LSTM网络的短时记忆独特特征对商品评论的情感分类取得了很好的效果,准确率达到99%以上.

Sentiment Analysis of Commodity Reviews Based on LSTM

With the development of e-commerce, a large number of reviews of goods have been produced. According to short text features of commodity reviews, emotion classification based on sentiment dictionary needs a lot of emotion database resources, and machine learning method needs complex artificial design features and feature extraction process. This study adopts the short and long term memory network (Long Short Term Memory, LSTM) text classification algorithm sentiment analysis. Firstly, the text word vector is introduced into LSTM network by using Word2Vec and word segmentation technology, and finally the classification model is obtained by Dropout algorithm. Experiments show that:in deep learning based sentiment analysis of commodity reviews, the unique characteristics of short-term memory network have sound results on commodity reviews sentiment classification, the accuracy of classification is more than 99%.