| 面向新浪微博的意见领袖挖掘算法 |
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| 引用本文： | 刘俊杰,马 畅,邵维龙,韩东红,夏 利. 面向新浪微博的意见领袖挖掘算法[J]. 计算机与现代化, 2018, 0(9): 80. DOI: 10.3969/j.issn.1006-2475.2018.09.016 |
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| 作者姓名： | 刘俊杰 马 畅 邵维龙 韩东红 夏 利 |
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| 基金项目： | 国家自然科学基金资助面上项目(61173029, 61672144) |
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| 摘 要： | 当前的影响力分析算法大多基于网络拓扑结构或用户交互信息，然而单一方面的方法会使挖掘结果出现较大的偏差，目前缺乏全面准确的影响力挖掘方法。本文通过对传统PageRank算法进行扩展，提出一种面向新浪微博的基于用户交互度连接属性的TCRank算法；其次设计了3种微博意见领袖特征指标，并对其加权求和用于意见领袖候选集的精化操作；同时提出一种基于卷积神经网络模型的情感支持度的意见领袖抽取算法，对意见领袖候选集进行最终排名。最后，通过实验验证所提出算法的有效性。
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| 关 键 词： | 新浪微博 意见领袖 PageRank 特征指标 卷积神经网络 |
| 收稿时间： | 2018-09-30 |
Opinion Leader Mining Algorithms on Sina Weibo |
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| LIU Jun-jie,MA Chang,SHAO Wei-long,HAN Dong-hong,XIA Li. Opinion Leader Mining Algorithms on Sina Weibo[J]. Computer and Modernization, 2018, 0(9): 80. DOI: 10.3969/j.issn.1006-2475.2018.09.016 |
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| Authors: | LIU Jun-jie MA Chang SHAO Wei-long HAN Dong-hong XIA Li |
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| Abstract: | The current influence analysis algorithms are mostly based on network topology structure or user interaction information. However, a single method will lead to a large deviation in mining results. At present, there is no comprehensive and accurate influence mining method. Therefore, by extending the traditional PageRank algorithm, a new TCRank algorithm based on user interaction connection attribute is proposed for Sina Weibo. Secondly, three kinds of micro-blog opinion leader characteristics are designed, and their weighted summation is used to refine the candidate set of opinion leaders. At the same time, an opinion leader extraction algorithm based on emotional support of convolution neural network model is proposed to rank the candidate set of opinion leaders. Finally, the effectiveness of the proposed algorithm is verified by experiments. |
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| Keywords: | Sina Weibo opinion leader PageRank characteristic indexes convolution neural network |
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