發(fā)布時間：2018-05-30 15:10

  本文選題：文本分類 + 興趣識別��； 參考：《北京交通大學》2014年碩士論文

【摘要】：微博是一個基于用戶關系的信息分享、傳播以及獲取平臺,內容簡單、用戶之間的交互性強、使用門檻低是微博的特點,近年在我國取得了快速發(fā)展。微博作為當下最流行的社會化網絡服務媒體,基于微博的用戶興趣挖掘研究也迅速成為一個新興的研究課題：首先發(fā)現(xiàn)感興趣的微博賬戶與信息是微博用戶最重要的活動,微博平臺則需要準確地基于用戶的興趣進行相關信息的推薦；其次用戶興趣識別系統(tǒng)是實現(xiàn)精準廣告投放的基礎,興趣挖掘的準確度直接關系到廣告投放的效果,關系到微博平臺的盈利。 本文作者在分析新浪微博的信息特點和用戶行為特點的基礎上,學習了傳統(tǒng)的使用詞向量進行文本特征表示的文本分類算法,并基于無監(jiān)督、無層次結構的主題模型LDA (Latent Dirichlet Allocation,隱含狄利克雷分配),擴展實現(xiàn)了有監(jiān)督、無層次結構的主題模型Labeled LDA,用于對微博用戶興趣分布的識別。本文主要研究了用戶興趣識別過程中涉及到的關鍵問題,主要包括以下三個方面的工作：(1)使用Python開發(fā)針對新浪微博的定制網絡爬蟲,繞開微博API的限制,實現(xiàn)微博文本的并發(fā)快速獲取,為研究工作提供了及其豐富的實驗數(shù)據(jù)；(2)學習文本分類技術,使用有監(jiān)督、無層次結構的主題模型Labeled LDA,用微博主題賬號的微博文本進行模型的訓練,用于對其他微博用戶興趣的預測；(3)考慮海量數(shù)據(jù)的場景,使用Hadoop、Hive等分布式框架,實現(xiàn)海量中文文本數(shù)據(jù)的分布式分詞與預處理。最終,通過用戶興趣識別系統(tǒng)得到的用戶興趣分布的數(shù)據(jù)在實際中成功的應用于用戶個性化詞云的產生與展現(xiàn)、搜索結果的調整與優(yōu)化、廣告的個人興趣化定向投放等方面。
[Abstract]:Weibo is a platform for information sharing, dissemination and acquisition based on user relationship. It is characterized by simple content, strong interaction between users and low threshold of use of Weibo. In recent years, it has achieved rapid development in China. Weibo as the most popular social network service media, the research of user interest mining based on Weibo has quickly become a new research topic: first of all, it is the most important activity of Weibo users to discover the interesting Weibo account and information. The Weibo platform needs to recommend the relevant information accurately based on the user's interest. Secondly, the user interest recognition system is the basis for the implementation of accurate advertising, and the accuracy of interest mining is directly related to the effect of advertising. Related to the Weibo platform profit. On the basis of analyzing the information characteristics and user behavior characteristics of Sina Weibo, the author of this paper studies the traditional text classification algorithm using word vector for text feature representation, and based on unsupervised, The topic model LDA / Latent Dirichlet allocation without hierarchy is extended to implement a supervised and unhierarchical topic model, Labeled LDA. it is used to identify the distribution of interest of Weibo users. This paper mainly studies the key problems involved in the process of user interest identification, including the following three aspects: 1) using Python to develop customized web crawlers for Sina Weibo, circumventing the limitations of Weibo API. To realize the concurrency and fast acquisition of Weibo text, this paper provides an extremely rich experimental data for the research work and studies the text classification technology, which is supervised. The hierarchical topic model Labeled LDAuses the Weibo text of the Weibo theme account to train the model, which is used to predict the interest of other Weibo users. (3) considering the scene of massive data, using the distributed framework such as Hadoop Hive, etc. Distributed word segmentation and preprocessing of massive Chinese text data are realized. Finally, the user interest distribution data obtained through the user interest recognition system has been successfully applied to the generation and presentation of user personalized word cloud, the adjustment and optimization of search results, and the orientation and placement of personal interest in advertising.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.092

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本文編號：1955743