基于CNN-BiLSTM的消费者网络评论情感分析

现如今,商品在线评论的情感分析业已成为许多商家不可忽视的重要工作,它对于商家了解用户偏好有着重要意义,同时,它还能够为相关产品下一步的改进工作提供方向指导.然而,传统的分析方法已无法解决现在情感分析中特征提取及语义理解等方面存在的问题.针对此类问题,本文提出一种基于CNN-BiLSTM的在线评论情感分析方法,不仅可以像LSTM一样建立时序关系,而且可以像CNN一样刻画局部空间特征.医疗服务、物流快递、金融服务、旅游住宿和食品餐饮数据集的实验结果表明,该方法能有效判别消费者在线评论情感倾向,在文本的情感分类中效果较传统机器学习算法更准确,F1值可以达到94.67%.同时,实验证明该方法具有较好的领域可拓展性.

Consumer reviews sentiment analysis based on CNN-BiLSTM

Nowadays, the sentiment analysis of online reviews of goods has become an important work that many businesses cannot ignore, which is of great significance for businesses to understand user preferences and can also provide directional guidance for the next improvement of related products. However, the traditional analysis methods have been unable to solve the problems of feature extraction and semantics understanding in sentiment analysis. Aiming at such problems, this paper proposes sentiment analysis of online reviews method based on CNN-BiLSTM, which can not only establish the sequential relationship like LSTM, but also describe local spatial characteristics like CNN. The experimental results on data sets of medical services, logistics express, financial services, tourist accommodation and food and beverage show that this method can effectively discriminate the emotional tendency of consumers' online reviews and is more accurate than traditional machine learning algorithm in sentiment classification in this paper and the F₁ value can reach 94.67%. Moreover, the experiments show that the method has good fields expansibility.