基于文本分类技术的微博平台潜在客户挖掘

发布时间：2018-05-03 04:16

本文选题：客户特性描述 + 社会关系　；参考：《广东外语外贸大学》2013年硕士论文

【摘要】：微博(Microblog)、Facebook和YouTube等社会化媒体的快速发展已经深刻地改变了企业与客户、客户与客户之间的沟通互动方式，在这种新兴媒体上，客户在产品或服务交易市场上发挥着空前主动的角色。社会化媒体具有强大的信息传播能力、互动性强、信息分享实时等特点，充分利用这些特点进行有效的社会化媒体营销能帮助企业改善品牌形象，提高品牌知名度，从而扩大其市场份额。微博的用户数量庞大、信息传播速度迅速、影响范围广泛，这使得微博营销成为企业社会化媒体营销中最为重要的一个环节，而潜在客户识别是开展精准微博营销的重要基础。如何有效地表示客户的特性是潜在客户挖掘最重要的基础问题，它对潜在客户挖掘效果具有决定性的作用。目前，，国内外对微博平台潜在客户挖掘的研究尚少，相关的研究主要根据客户的人口统计信息和微博使用行为等方面抽取特征来刻画客户的特性，该类型方法的操作较为复杂；同时，由于对客户特性的描述特征还不够准确等问题导致其识别准确率偏低（最好的准确率为76%左右）。本研究认为客户的社会关系网的兴趣爱好信息对客户特性的描述具有重要意义，旨在通过微博平台探索客户的社会关系特性在潜在客户挖掘中的作用，提出融合客户及其微博好友自定义标签信息，从客户个人和社会特性两个方面生成客户特性描述文本，进而提出一种基于文本分类的微博平台潜在客户挖掘框架。大量的实验结果表明：本研究提出的客户特性描述方法能帮助潜在客户识别模型平均有86%左右的准确率；K最近邻（K Nearest Neighbors，KNN）分类、朴素贝叶斯（Naive Bayes，NB）分类、Rocchio分类、基于类别质心的分类方法（Centroid-based Classification，Centroid）和支持向量机分类（Support VectorMachines， SVM）等5种文本分类算法都获得较高准确率的潜在客户识别效果，验证了本研究所提出框架的有效性。在这5个分类器中，SVM取得了准确率最高的潜在客户识别性能，但其建模和决策分析较为耗时，而NB是在潜在客户识别性能和运行时间方面权衡的最好的分类算法，其次为Rocchio和Centroid。借助微博平台提供的丰富社会关系信息，融合客户的社会关系网的兴趣爱好信息来刻画客户的特性不仅为潜在客户挖掘提供一种新的视角和手段，同时也为客户细分、客户流失等经典客户关系管理问题的研究提供很好的参考。
[Abstract]:The rapid development of social media, such as Weibo's microblog and YouTube, has profoundly changed the way businesses communicate and interact with customers, and in this emerging media, Customers in the product or service trading market plays an unprecedented active role. Social media has the characteristics of strong information dissemination ability, strong interaction, real-time information sharing, etc. Making full use of these characteristics for effective social media marketing can help enterprises to improve their brand image and brand awareness. To expand its market share. Weibo has a large number of users, rapid information dissemination and a wide range of influence, which makes Weibo marketing become the most important part of enterprise social media marketing, and potential customer identification is an important basis for accurate Weibo marketing. How to effectively express the characteristics of customers is the most important fundamental problem in mining potential customers, which plays a decisive role in mining potential customers. At present, there are few researches on the mining of potential customers of Weibo platform at home and abroad. The related studies mainly depict the characteristics of customers according to the demographic information of customers and the characteristics of Weibo's use behavior. The operation of this type of method is more complex. At the same time, due to the inaccurate description of customer characteristics, the recognition accuracy is low (the best accuracy is about 76%). This study holds that the interest and interest information of customer social network is of great significance to the description of customer characteristics, and aims to explore the role of customer social relationship characteristics in the mining of potential customers through Weibo platform. This paper proposes a framework for potential customer mining based on text categorization based on the integration of custom tag information of clients and their Weibo friends, and the generation of customer feature description text from two aspects of customer's personal and social characteristics. A large number of experimental results show that the proposed customer characteristic description method can help potential customer identification model with an average accuracy of about 86%. K nearest neighbor K Nearest neighbor KNN, naive Bayes Bayes NB) can be used to classify Rocchio classification. The classification methods based on classification centroid (Centroid-based Classification / Centroid) and support Vector machines (SVM-based) are all effective in identifying potential customers with high accuracy, which verifies the effectiveness of the proposed framework. Among the five classifiers, SVM has achieved the highest accuracy of potential customer identification performance, but its modeling and decision analysis are time-consuming. NB is the best classification algorithm to weigh the potential customer identification performance and running time, followed by Rocchio and Centroid. With the help of the rich social relationship information provided by Weibo platform and the interest and hobby information of the social network of integrating customers to depict the characteristics of customers, it not only provides a new perspective and means for potential customers to excavate, but also provides a new way for customer segmentation. Customer drain and other classic customer relationship management issues provide a good reference.
【学位授予单位】：广东外语外贸大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F274;G206

【参考文献】