Topic Detection of Single-Pass-SOM Combination Model Based on Multi Feature
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    Abstract:

    Nowadays, internet public opinion has a rapid spread and great influence, and topic detection plays an irreplaceable role in the supervision of public opinion. Aiming at the problems of incomplete feature extraction and high feature dimension in traditional methods, this study proposes LDA&&Word2Vec text representation model based on time decay factor, which combines the hidden subject features by LDA model with the semantic features by Word2Vec model, and adds time decay factor, which can reduce the dimension and improve the integrity of text features. At the same time, this study proposes a Single-Pass-SOM clustering model, which solves the problem of setting initial neurons in SOM model, and improves the accuracy of topic clustering. Experimental results show that the text representation model and text clustering method proposed in this study have better topic detection effect than traditional methods.

    Reference
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    [2] 路荣, 项亮, 刘明荣, 等. 基于隐主题分析和文本聚类的微博客中新闻话题的发现. 模式识别与人工智能, 2012, 25(3): 382-387. [doi: 10.3969/j.issn.1003-6059.2012.03.004
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    [4] 李新盼. 基于微博的网络舆情分析系统的设计与实现[硕士学位论文]. 成都: 电子科技大学, 2017.
    [5] 赵杨. 面向热点话题的舆情演化分析方法研究[硕士学位论文]. 哈尔滨: 哈尔滨工程大学, 2018.
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    [8] Kohonen T. The ‘neural’ phonetic typewriter. Computer, 1988, 21(3): 11-22. [doi: 10.1109/2.28
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李丰男,孟祥茹,焦艳菲,张琳琳,刘念.基于多特征融合Single-Pass-SOM组合模型的话题检测.计算机系统应用,2020,29(7):245-250

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History
  • Received:December 18,2019
  • Revised:January 14,2020
  • Online: July 04,2020
  • Published: July 15,2020
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