欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
通过建立模型对电商企业的客户查询信息进行文本分类分析，帮助企业掌握用户的消费习惯，同时帮助用户及时找到需要的商品．本文首先获取客户查询数据并对该文本数据进行预处理，利用改进的TF—IDF方法获得文本特征向量，最后结合朴素贝叶斯文本分类及半监督的EM迭代算法建立分类模型，并应用各种标准对模型进行评估，验证模型的有效性．多类别文本集选取文本特征时，关键词权值容易产生波动，本研究改进关键词权值计算公式来改善分类结果．实验结果表明分类器具有良好的分类效果．
英文摘要：
In this paper, we establish a model to analysis business enterprise customer query information for text classification to help e -commerce companies control the user＇s spending habits, and help users to find their needed goods. This study accesses to customer inquiry data and preprocesses these text data firstly. And then, the improved TF -IDF principle is applied to obtain the text feature vectors. Finally, this study establishes the classification model combining the Naive Bayes text classification and the semi - supervised EM iterative algorithm, and uses various criteria to evaluate the model. When facing multi - class text classification feature selection, keyword weights prone to great volatility. This study improves the keyword weight calculation formula to perfect the classification results. The experimental results show that classification has good classification effect.
同期刊论文项目
同项目期刊论文
期刊信息
位置：