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数据分析与知识发现  2018, Vol. 2 Issue (12): 52-59     https://doi.org/10.11925/infotech.2096-3467.2018.0415
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于CART决策树的网络问答社区新兴话题识别研究*
程秀峰1, 张心怡2, 王宁2()
1中国科学技术信息研究所 北京 100038
2华中师范大学信息管理学院 武汉 430079
Identifying Trending Topics in Q&A Community with CART Decision Tree
Cheng Xiufeng1, Zhang Xinyi2, Wang Ning2()
1Institute of Scientific and Technical Information of China, Beijing 100038, China
2School of Information Management, Central China Normal University, Wuhan 430079, China
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摘要 

【目的】协助相关决策部门监督和管理网络舆情, 探测可能成为舆情关注焦点的新兴话题。【方法】提出网络问答社区中新兴话题的识别标准和依据, 并基于知乎问答社区, 利用CART决策树对识别过程进行实证研究。【结果】对于网络问答社区, CART决策树在新兴话题的识别与预测方面具有较好的准确性和适用性。【局限】实验数据只占知乎所有话题板块的一小部分, 为验证该方法的有效性, 需要进一步扩展数据集。【结论】基于CART决策树的网络问答社区新兴话题识别方法能够有效预测新兴话题, 可为网络问答社区的热点话题筛选机制提供参考。

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程秀峰
张心怡
王宁
关键词 决策树网络问答社区新兴话题    
Abstract

[Objective] This paper tries to identify the trending topics, aiming to help the decision-making agencies manage online public opinion. [Methods] Firstly, we proposed the criteria to detect the trending topics of Q&A community. Then, we conducted an empirical study on China’s Zhihu Q&A community using the CART decision tree algorithm. [Results] The CART decision tree predicted the trending topics. [Limitations] We only collected data from a small portion of all topics on Zhihu. More data is needed for future studies. [Conclusions] The proposed method based on the CART decision tree algorithm could effectively predict trending topics in the Q&A community, which help us choose popular contents.

Key wordsDecision Tree    Q&A Community    Trending Topics
收稿日期: 2018-04-13      出版日期: 2019-01-16
ZTFLH:  G25  
基金资助:*本文系国家自然科学青年基金项目“基于QSIM的图书馆移动用户群体行为模拟与学习兴趣引导研究”(项目编号: 7150309)和教育部人文社会科学研究青年基金项目“移动环境下图书馆用户行为发现与知识推荐研究”(项目编号: 14YJC870004)的研究成果之一
引用本文:   
程秀峰, 张心怡, 王宁. 基于CART决策树的网络问答社区新兴话题识别研究*[J]. 数据分析与知识发现, 2018, 2(12): 52-59.
Cheng Xiufeng,Zhang Xinyi,Wang Ning. Identifying Trending Topics in Q&A Community with CART Decision Tree. Data Analysis and Knowledge Discovery, 2018, 2(12): 52-59.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0415      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I12/52
特征 一级标准 二级标准
①吸引力较强 A问题关注度 A1浏览次数
②参与度较高 A2关注人数
③影响力较大 A3回答数量
④内容多样性 B问题内聚度 B1回答相近度
⑤间隔时间短 B2粒度特征值
⑥具备关键节点 C问题影响度 C1用户关键度
⑦传播速度快
  新兴话题识别模式特征—标准对应表
Title Focus Cohesion Impact Is Order
Tree1.Topic1 1 379 560 8.99 502 643 1 25
Tree1.Topic2 9 495 9.81 83 591 1 49
Tree1.Topic3 356 204 8.04 522 595 1 55
Tree1.Topic4 51 740 8.23 89 478 1 63
Tree1.Topic5 347 185 9.28 994 162 1 93
Tree1.Topic6 3 496 1.98 8 874 1 94
Tree1.Topic7 8 538 3.64 597 1 96
Tree1.Topic8 4 361 4.41 10 818 1 99
Tree1.Topic9 56 159 3.33 93 735 1 110
Tree1.Topic10 35 877 1.82 21 288 1 115
Tree1.Topic11 6 600 5.86 5 318 1 118
Tree1.Topic12 403 128 8.66 97 756 1 121
Tree1.Topic13 4 249 1.89 1 108 1 124
Tree1.Topic14 703 195 15.20 52 308 1 128
Tree1.Topic15 1 327 4.31 2 760 1 136
  预处理的T1问题数据
Title Focus Cohesion Impact Is Order
Tree1.Topic16 109 452 15.91 109 622 0 137
Tree1.Topic17 95 0 0 0 139
Tree1.Topic18 648 7.18 457 0 145
Tree1.Topic19 5 068 3.49 111 0 149
Tree1.Topic20 950 3.27 11 670 0 153
Tree1.Topic21 801 1.53 46 0 159
Tree1.Topic22 1 472 1.97 44 0 163
Tree1.Topic23 791 2.37 586 0 164
Tree1.Topic24 426 1.83 12 650 0 173
Tree1.Topic25 281 1.85 68 0 180
Tree1.Topic26 871 3.39 5 181 0 203
Tree1.Topic27 1 196 2.11 144 588 0 207
Tree1.Topic28 576 3.13 9 949 0 209
Tree1.Topic29 408 1.95 16 350 0 213
Tree1.Topic30 463 2.46 465 0 234
  预处理的T2问题数据
Title Focus Cohesion Impact Is Order
Tree2.Topic1 109 452 15.91 109 622 1 1
Tree2.Topic2 403128 8.66 97 756 1 8
Tree2.Topic3 14 593 5.26 2 347 1 15
Tree2.Topic4 2 357 3.28 36 327 1 22
Tree2.Topic5 217 4.29 2 751 1 29
Tree2.Topic6 233 3.92 1 178 1 36
Tree2.Topic7 165 4.00 700 1 43
Tree2.Topic8 82 4.36 1 182 1 50
Tree2.Topic9 3 496 1.98 8 874 1 57
Tree2.Topic10 151 2.77 1 156 1 64
Tree2.Topic11 170 3.03 390 1 71
Tree2.Topic12 426 1.82 12 650 1 78
Tree2.Topic13 294 3.46 59 1 85
Tree2.Topic14 135 2.98 246 1 92
Tree2.Topic15 141 2.54 322 1 99
  预处理的T3问题数据
Title Focus Cohesion Impact Is Order
Tree2.Topic16 156 1.82 8 982 0 106
Tree2.Topic17 102 3.78 51 0 113
Tree2.Topic18 309 1.64 1 141 0 120
Tree2.Topic19 865 1.34 2 178 0 127
Tree2.Topic20 161 2.68 39 0 134
Tree2.Topic21 75 3.26 54 0 141
Tree2.Topic22 187 2.68 27 0 148
Tree2.Topic23 87 2.04 169 0 155
Tree2.Topic24 57 1.81 1 350 0 162
Tree2.Topic25 117 2.03 47 0 169
Tree2.Topic26 56 1.78 405 0 176
Tree2.Topic27 31 1.91 1 091 0 183
Tree2.Topic28 130 1.99 18 0 190
Tree2.Topic29 59 1.76 239 0 197
Tree2.Topic30 93 1.71 64 0 204
  预处理的T4问题数据
  决策树Tree1
  决策树Tree2
  两棵决策树预测新兴话题的准确率对比情况
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