微博用户的兴趣发现与意图识别的研究与实现

发布时间：2018-03-18 20:17

本文选题：用户兴趣　切入点：LDA　出处：《北京邮电大学》2017年硕士论文　论文类型：学位论文

【摘要】：以微博为代表的社交网络平台在日常生活中越来越活跃。微博平台上众多微博用户实时产生的海量微博内容,带来了信息冗余的问题,给用户的使用体验带来挑战。发现微博用户个人喜好和兴趣,为用户带来效率更高、更精确的使用体验;识别微博用户的行为意图,为微博营销平台提供更加精准的指导,是微博研究中两个重大问题。本文较为深入地研究这两个问题,提出了解决方案。发现微博用户的兴趣,面对的主要问题是微博文本的短文本特性。传统文本处理方法在特征稀疏、微博文本上下文依赖性问题上有着局限性。本文基于传统LDA特征拓展方法进行改进,将文本-主题分布中的文本主题特征引入文本特征空间,进一步拓展特征。利用主题模型识别微博中的多义词,消除多义词影响,进一步提升分类算法的性能。通过实验来验证方法的有效性。识别微博用户的意图,是微博研究中较为新颖的领域。在本研究中,先提取潜在的意图微博,根据意图微博特征,将包含意图指示词的微博提取出来。然后根据图传播模型对微博进行意图分类。并通过实验来验证该方法的有效性。
[Abstract]:The social network platform, represented by Weibo, is becoming more and more active in daily life. The huge amount of Weibo content generated in real time by a large number of Weibo users on the Weibo platform has brought about the problem of information redundancy. It brings challenges to the user's use experience. It is found that Weibo's personal preferences and interests bring users a more efficient and accurate use experience; identify the user's behavior intention; and provide more precise guidance for Weibo's marketing platform. It is two major problems in Weibo's research. This paper studies these two problems in depth and puts forward a solution. We find the interest of Weibo users. The main problem is the short text feature of Weibo text. The traditional text processing method has some limitations in feature sparsity, Weibo text context-dependent problem. This paper improves on the traditional LDA feature extension method. The text theme features in the text-theme distribution are introduced into the text feature space to further expand the features. The polysemous words in Weibo are identified by the thematic model to eliminate the influence of polysemous words. To further improve the performance of the classification algorithm. Through experiments to verify the effectiveness of the method. Identification of Weibo user intention, is a relatively new area of research. In this study, the potential intention of Weibo is extracted, according to the characteristics of the intention Weibo, Weibo was extracted from the deixis of intention. Then the intention was classified according to the graph propagation model, and the validity of the method was verified by experiments.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP393.092;TP391.1

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