基于评论大数据的手机产品改进

Mobil phone product improvement based on big data of comment

为应对高频产品迭代,提出一种基于评论大数据的产品设计改进研究方法。以手机为研究对象爬取大量在线评论,借助隐含狄利克雷主题模型确定用户关注的产品属性及其对应的属性词,并对评论的有用性进行分析;通过情感分析计算用户对手机各产品属性的关注度和满意度等指标,从而建立评价指标体系,并找到手机亟需改进的产品属性;通过观点挖掘找到用户不满意的点,进而确定手机的改进策略。利用多元线性回归分析建立手机评价指标与评论差评率的线性回归模型,通过采用该模型较准确地预测手机评论的差评率,验证了所提方法的有效性。该方法将挖掘和情感分析结合,可快速为产品设计改进提供决策依据。

In response to the high frequency product iterations today,a research method on improvement of product design based on big data of comment was proposed.Taking mobile phone for research object,a large number of online comments were crawled.The product attributes concerned by users and their corresponding attribute words were determined by Latent Dirichlet Allocation,and comment helpfulness analysis was conducted.Through sentiment analysis,the relevant indicators such as the user's attention and satisfaction to each product attribute of phones were calculated to establish evaluation indicator system,and the product attributes of phones that needed to be improved were found.The user's dissatisfaction was found by opinion mining to determine the phone improvement strategy.A linear regression model between the phone evaluation indicators and the rate of negative comments was established through multiple linear regression analysis.The model could be used to accurately predict the rate of negative comments,which verified the effectiveness of the proposed method.By combining text mining and sentiment analysis,the method could quickly provide decision-making basis for improvement of product design.