基于网络用户评论的评分预测模型研究

发布时间：2018-04-29 05:08

  本文选题：评分预测 + 情感分析　； 参考：《数据分析与知识发现》2017年08期

【摘要】：【目的】通过网络用户评论,为评论网站构建有效的评分预测机制。【方法】提出基于网络用户评论的评分预测模型,该模型包括4个模块:网络用户评论获取模块、预测变量获取模块、预测分析模块以及预测结果评价模块。抓取30部不同类型的电影评论数据,27部用于构建模型,3部用于检验模型。【结果】使用逐步回归方法筛选出变量:参与评分人数、参与评论人数、想要观看人数和电影正向评论情感均值,构建评分预测模型。使用3部电影验证,预测评分与IMDb评分相差最大值为0.0644,最小值为0.0227。【局限】在数据样本量、情感特征提取精度、模型普适性验证等方面有待进一步提升。【结论】该模型能够依据用户评论对评分进行有效预测,在网络水军探测方面也能发挥一定的作用。
[Abstract]:[objective] to construct an effective scoring prediction mechanism for the comment website through the network user comments. [methods] A scoring prediction model based on the network user comment is proposed. The model includes four modules: the network user comment acquisition module. Prediction variable acquisition module, prediction analysis module and prediction evaluation module. Grabbing 30 different types of movie review data, 27 were used to build models and 3 were used to test the models. [results] the variables were screened out using stepwise regression: number of participants, number of comments, In order to evaluate the number of viewers and the emotional average of positive reviews, a rating prediction model was constructed. Using three films to verify, the difference between prediction score and IMDb score is 0.0644, the minimum value is 0.0227.The accuracy of data sample size, emotion feature extraction, [conclusion] the model can effectively predict the score according to the user comments, and it can also play a certain role in the detection of the network navy.
【作者单位】： 中山大学资讯管理学院;
【基金】：国家社会科学基金项目“用户评论情感分析及其在竞争情报服务中的应用研究”(项目编号:11CTQ022) 广东省科技专项“基于内容的科技文献分析服务平台”(项目编号:2016B030303003)的研究成果之一
【分类号】：G252
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本文编号：1818518