摘要
【目的】以众测报告为研究对象,探索众测报告作者属性、产品属性、文本、图片对预测众测报告有用性的作用。【方法】基于深度学习提取众测报告的文本特征和图片特征,使用全连接神经网络构建众测报告有用性预测模型,使用80%随机样本对不同输入组合下的模型进行训练学习,再以剩余样本作为测试集评估模型的预测效果。【结果】单独加入文本特征后,模型的预测效果提升4.24%;单独加入图片特征后,模型的预测效果提升5.21%;同时加入文本特征和图片特征后,模型的预测效果提升6.96%。【局限】深度学习提取的文本特征和图片特征可理解性与可解释性较差,因此,即使最终模型的预测结果比较准确,仍难以得知模型中每一层神经网络所代表的具体特征并总结归纳出模型做出最终决策所依赖的预测规则。【结论】众测报告中文字描述的特征和图片特征都能有效预测众测报告对消费者的有用性,且两者对于预测众测报告对消费者的有用性具有相互验证和相互替代的作用。
[Objective]This paper tries to predict the usefulness of crowd testing reports with author attributes,text features,and image features.[Methods]First,we adopted deep learning techniques to extract text and image features from crowd testing reports.Then,we constrcuted a prediction model with full-connected neural network.Third,we trained the new model with 80%of samples and different input combinations.Finally,we examined our model’s performance with the remaining samples.[Results]With the help of text or image features,the prediction accuracy of the model increased by 4.24%and 5.21%,respectively.Using both the text and image features,our model’s prediction accuracy increased by 6.96%.[Limitations]The extracted features of texts and images were not understandable and interpretable.Therefore,we cannot identify specific features represented by each layer of neural network in the model.[Conclusions]The proposed model with text and image features can effectively predict the usefulness of crowd testing reports.
作者
蔡婧璇
吴江
王诚坤
Cai Jingxuan;Wu Jiang;Wang Chengkun(School of Information Management,Wuhan University,Wuhan 430072,China;Center for E-commerce Research and Development,Wuhan University,Wuhan 430072,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第11期102-111,共10页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目“信息不对称驱动的共享经济去中心化机制与风险的复杂性研究”(项目编号:71874131)的研究成果之一。
关键词
产品众测
信号理论
深度学习
特征提取
预测分析
Crowd Testing
Signal Theory
Deep Learning
Feature Extraction
Predictive Analysis
