| 基于情绪和兴趣的用户访问行为预测 |
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| 引用本文： | 秦锋,陈增,郑啸,童琨.基于情绪和兴趣的用户访问行为预测[J].计算机系统应用,2018,27(1):28-34. |
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| 作者姓名： | 秦锋 陈增 郑啸 童琨 |
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| 作者单位： | 安徽工业大学 计算机科学与技术学院, 马鞍山 243032,安徽工业大学 计算机科学与技术学院, 马鞍山 243032,安徽工业大学 计算机科学与技术学院, 马鞍山 243032,安徽祥云科技有限公司, 马鞍山 243032 |
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| 基金项目： | 国家自然科学基金（61402008）；安徽省高校自然科学研究重大项目（KJ2014ZD05）；安徽省高校优秀青年人才支持计划；安徽省科技重大专项（16030901060） |
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| 摘 要： | 微博用户行为预测旨在研究用户的行为习惯，本文主要从用户属性、用户兴趣和用户情绪三个方面，对影响微博用户行为的因素进行研究分析，提取影响用户行为的特征，训练预测模型. 实验中还将情感和兴趣特征在预测模型中的作用进行了对比，结果显示预测模型在转发行为预测的平均准确率能够达到82.56%，在评论行为预测的平均准确率能够达到84.59%，在点赞行为预测的平均准确率能够达到79.35%，表明了用户兴趣和情感特征对于微博用户行为预测结果提升中的有效性.
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| 关 键 词： | 用户行为 微博 情感分析 兴趣 预测 |
| 收稿时间： | 2017/3/30 0:00:00 |
| 修稿时间： | 2017/4/20 0:00:00 |
User Behavior Prediction Based on Emotion and Interest |
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| QIN Feng,CHEN Zeng,ZHENG Xiao and TONG Kun.User Behavior Prediction Based on Emotion and Interest[J].Computer Systems& Applications,2018,27(1):28-34. |
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| Authors: | QIN Feng CHEN Zeng ZHENG Xiao and TONG Kun |
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| Affiliation: | College of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China,College of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China,College of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China and Anhui Xiangyun Technology Co. Ltd., Maanshan 243032, China |
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| Abstract: | Micro-blog user behavior prediction aims to study user behavior habits. This paper mainly studies the factors that affect the behaviors of users of microblogging from three aspects: the user attribute, user interest, and user''s emotion. We extract the characteristics of the user behaviors, training and forecasting the model. The experimental results show that the average accuracy of forwarding behavior can reach 82.56% in the prediction, the average prediction accuracy of behavior in the comments reaching 84.59%, the prediction average accuracy of likes behavior rate reaching 79.35%, which indicates the effectiveness of user interest and emotion characteristics in the promotion of microblogging user behavior prediction. |
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| Keywords: | user behavior micro-blog sentiment analysis interest prediction |
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