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Automatic Generation of Review Content in Specific Domain of Social Network Based on RNN

机译:基于RNN的社交网络特定领域评论内容的自动生成

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The online social network has become a favorable site where a large number of malicious netizens spread rumors and conduct malignant competition. In this paper, we set up a method for generating review content in specific domain of social networks, which uses a recurrent neural network model to generate the social network-style review. Taking Twitter platform as an example platform, we firstly classify the review text according to the sentence pattern; secondly, aiming at different categories, we design corresponding recurrent neural network model to generate the initial review text corresponding to sentence structures; finally, we conduct automatic replacement of the generated initial text through the relevance of subject terms to achieve the effect of better adapting to hot topics. This method is not only easy to operate and economical, but also can evade the most advanced detectors. In the same environment, it is superior to the existing technology and generates more than 85.2% of the output text with correct grammar and wise contents.
机译:在线社交网络已成为一个有利的站点,大量恶意网民散布谣言并进行恶性竞争。在本文中,我们建立了一种在社交网络特定领域中生成评论内容的方法,该方法使用递归神经网络模型来生成社交网络样式的评论。以Twitter平台为例,首先根据句子模式对评论文本进行分类。其次,针对不同的类别,设计相应的递归神经网络模型,生成与句子结构相对应的初始评论文本。最后,我们通过主题词的相关性自动替换生成的初始文本,以达到更好地适应热门话题的效果。这种方法不仅易于操作且经济,而且可以逃避最先进的检测器。在相同的环境中,它优于现有技术,并以正确的语法和明智的内容生成超过85.2%的输出文本。

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