基于层次注意力机制神经网络模型的虚假评论识别

针对虚假评论识别任务中传统离散模型难以捕捉到整个评论文本的全局语义信息的问题,提出了一种基于层次注意力机制的神经网络模型。首先,采用不同的神经网络模型对评论文本的篇章结构进行建模,探讨哪种神经网络模型能够获得最好的篇章表示;然后,基于用户视图和产品视图的两种注意力机制对评论文本进行建模,用户视图关注评论文本中用户的偏好,而产品视图关注评论文本中产品的特征;最后,将两个视图学习的评论表示拼接以作为预测虚假评论的最终表示。以准确率作为评估指标,在Yelp数据集上进行了实验。实验结果表明,所提出的层次注意力机制的神经网络模型表现最好,其准确率超出了传统离散模型和现有的神经网络基准模型1至4个百分点。

Deceptive review detection via hierarchical neural network model with attention mechanism

Concerning the problem that traditional discrete models fail to capture global semantic information of whole comment text in deceptive review detection, a hierarchical neural network model with attention mechanism was proposed. Firstly, different neural network models were adopted to model the structure of text, and which model was able to obtain the best semantic representation was discussed. Then, the review was modeled by two attention mechanisms respectively based on user view and product view. The user view focused on the user's preferences in comment text and the product view focused on the product feature in comment text. Finally, two representations learned from user and product views were combined as final semantic representation for deceptive review detection. The experiments were carried out on Yelp dataset with accuracy as the evaluation indicator. The experimental results show that the proposed hierarchical neural network model with attention mechanism performs the best with the accuracy higher than traditional discrete methods and existing neural benchmark models by 1 to 4 percentage points.