计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 69-74.doi: 10.11896/jsjkx.190700034
冯进展, 蔡淑琴
FENG Jin-zhan, CAI Shu-qin
摘要： 由于无法预知产品在线评论的文本内容是否对浏览者有用,大量的无用评论增加了潜在消费者的信息搜索成本,甚至降低了潜在消费者购买产品的可能性。为提高电子商务平台的有用在线评论率,为撰写评论者提供测试功能,建立在线评论有用程度预测模型。根据在线评论的文本特征,所提模型选择在线评论的词语数量、词语的有用值、产品特征数量等3个特征,构建一个预测在线评论有用程度的模型,其中词语的有用值是词语区分在线评论有用程度的信息增益量,然后根据大量在线评论数据利用梯度下降算法解出模型参数。实验结果显示,随着词语数量、词语有用值、产品特征数量的增长,评论有用程度不断提高。实验中把在线评论分为一般、有用、非常有用3个程度,对于一般的在线评论,预测精确率为92.96%;对于“有用”在线评论,预测精确率为94.83%;对于“非常有用”在线评论,预测精确率为67.63%。实验对模型性能进行测试,得到平均精确率为85.05%,召回率为82.81%,F1值为83.72%,该结果验证了所提模型预测在线评论有用程度的可行性。
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