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Joint sentiment/topic modeling on text data using a boosted restricted Boltzmann Machine

机译：使用增强型受限玻尔兹曼机对文本数据进行情感/主题联合建模

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摘要

Recently by the development of the Internet and the Web, different types of social media such as web blogs become an immense source of text data. Through the processing of these data, it is possible to discover practical information about different topics, individual's opinions and a thorough understanding of the society. Therefore, applying models which can automatically extract the subjective information from documents would be efficient and helpful. Topic modeling methods and sentiment analysis are the raised topics in natural language processing and text mining fields. In this paper a new structure for joint sentiment-topic modeling based on a Restricted Boltzmann Machine (RBM) which is a type of neural networks is proposed. By modifying the structure of RBM as well as appending a layer which is analogous to sentiment of text data to it, we propose a generative structure for joint sentiment topic modeling based on neural networks. The proposed method is supervised and trained by the Contrastive Divergence algorithm. The new attached layer in the proposed model is a layer with the multinomial probability distribution which can be used in text data sentiment classification or any other supervised application. The proposed model is compared with existing models in the experiments such as evaluating as a generative model, sentiment classification, information retrieval and the corresponding results demonstrate the efficiency of the method.
机译：近年来，随着Internet和Web的发展，不同类型的社交媒体（例如Web博客）成为文本数据的巨大来源。通过处理这些数据，可以发现有关不同主题，个人意见和对社会的透彻了解的实用信息。因此，应用能够自动从文档中提取主观信息的模型将是高效且有帮助的。主题建模方法和情感分析是自然语言处理和文本挖掘领域中提出的主题。本文提出了一种基于受限玻尔兹曼机（RBM）的神经网络联合情感话题建模新结构。通过修改RBM的结构并在其上添加类似于文本数据情感的图层，我们提出了一种基于神经网络的联合情感主题建模的生成结构。所提出的方法由对比发散算法进行监督和训练。所提出的模型中的新附加层是具有多项式概率分布的层，可用于文本数据情感分类或任何其他受监督的应用程序。将所提出的模型与现有模型进行了实验比较，如生成模型的评估，情感分类，信息检索以及相应的结果证明了该方法的有效性。

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