产品评论情感倾向性分类系统的设计与实现

针对消费者网上购物时选择商品的用户体验不足等问题,设计了产品评论情感倾向性分类系统。系统首先对评论进行了分词,然后根据停用词表去停用词,分别采用CHI、IG进行特征选择,最后比较了使用不同的特征选择算法对文本情感分类结果产生的影响。系统采用了Java Web相关技术实现了可视化,并对产品评论的分类过程进行了展示。实验结果表明,有效的特征选择方法有助于提升推荐系统的性能。

Design and Implentation of Product Review Sentiment Classification System

For the insufficient of the customers experience when shopping on-line, this paper designs a product review sentiment classification system. The system segments the reviews firstly. Then according to the stop word list, the system removes the stop words. The system selects the features by using CHI method and IG method respectively. At last, the paper compares the effects produced by the text sentiment classification results using the different feature selection algorithms. The system uses Java Web related technologies to realize the visualization, and presents the classification process on product reviews. Experiment results show that the effective feature selection methods can help to enhance the performance of the recommendation system.