基于微博的消费意图挖掘

发布时间：2018-03-05 17:11

  本文选题：消费意图挖掘　切入点：SVM算法　出处：《哈尔滨工业大学》2014年硕士论文　论文类型：学位论文

【摘要】：微博作为一种新型的社交媒体，已经积聚了大量的用户和影响力。由于发布微博简单方便，传播速度快，微博上的用户发布了大量的内容丰富的信息。这些信息中有相当数量都表现了用户对某种商品的购买愿望，也就是消费意图。这些具有消费意图的文本数据对科学研究和商业应用都有着极高的价值。另外，这些文本也对社交媒体中的预测任务有着重要的意义。 本文中，针对基于基于微博的消费意图挖掘进行了以下三方面的研究： （1）消费意图语料获取及分类。文中首先探讨了消费意图初始语料的获取方法，并在一淘求购，京东和微博上获取了消费意图初始语料，并对语料进行了预处理。本文将消费意图视为一个二元分类问题，使用获取的消费意图语料抽取了多个维度的特征。最后，本文提出了基于SVM，Na ve Bayes以及深度学习（Deep Learning）的消费意图分类模型。其中，基于深度学习的消费意图分类方法的F值（F-measure）最高。 （2）消费意图到行为转化。在之前的实验中，消费意图正例采用人工标注的方式获得。然而，虽然制定了消费意图标注标准，但是在多人标注的过程中仍然存在标注结果不统一的问题。而且，，即便用户表达出了消费意图，也不代表用户一定会实施消费行为。本文中提出了一种基于社交媒体的大规模调查问卷发放方法，从社交媒体上自动采集了大量用户消费行为数据。这些数据被用于评价之前的消费意图分类模型，并用于构建消费行为分类器。 （3）消费意图于预测任务上的应用。本文中探讨了一类特定产品（即电影）的消费意图，并将电影消费意图应用于电影预测票房的任务上。实验结果表明，通过结合消费意图特征和传统方法中用于预测票房的特征，我们的模型取得了超过所有前人工作的R值。另外，我们还构建了一个电影票房预测系统，该系统从多个数据源自动采集数据并进行分析处理，最终在每部电影上映前给出该电影的票房预测结果。
[Abstract]:Weibo, as a new type of social media, has accumulated a lot of users and influence. Users on Weibo have published a large amount of rich information. A considerable amount of this information shows the user's desire to buy a certain product. That is, consumer intent. These consumptive text data are of great value for both scientific research and business applications. In addition, these texts are important for social media prediction tasks. In this paper, the consumption intention mining based on Weibo is studied in the following three aspects:. In this paper, we first discuss the methods of obtaining the initial data of consumption intention, and obtain the initial data of consumption intention on JingDong and Weibo. In this paper, the consumption intention is regarded as a binary classification problem, and the features of multiple dimensions are extracted by using the obtained consumption intention corpus. Finally, In this paper, a classification model of consumption intention based on SVMNve Bayes and Deep Learning Learning is proposed, in which the F-measure is the highest in the classification of consumption intention based on Deep Learning. In previous experiments, consumption intention was obtained by manual labeling. However, although the criteria for consumer intention labeling were established, However, there is still the problem of inconsistent labeling results in the process of multi-person tagging. Moreover, even if the user expresses his intention to consume, Nor does it necessarily mean that users will commit to consumer behavior. In this paper, a method of distributing large-scale questionnaires based on social media is proposed. A large number of consumer behavior data are automatically collected from social media. These data are used to evaluate the previous consumer intention classification model and to construct consumer behavior classifier. In this paper, we discuss the consumption intention of a class of specific products (that is, film), and apply it to the task of predicting the box office. The experimental results show that, By combining the consumption intention feature with the features used to predict the box office in the traditional method, our model has achieved a higher R value than all previous work. In addition, we have also constructed a film box office prediction system. The system automatically collects data from multiple data sources and analyzes and processes, and finally gives the box office prediction results of each movie before it is released.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.092;TP311.13


本文编号：1571153