Consumers are increasingly influenced by product reviews when purchasing goods or services. At the same time, deceptive reviews usually mislead users. It is inefficient and inaccurate to manually identify deceptive reviews in massive reviews. Therefore, automatically identifying deceptive reviews has become a research trend. Most of existing methods are less effective since they are lack of deeply understanding of reviews. We propose a neural network method with bidirectional long short-term memory (BiLSTM) and feature combination to learn the representation of deceptive reviews. We conduct a large amount of experiments and demonstrate the effectiveness of our proposed method. Specifically, in the mixed-domain detection experiment, the results prove that our model is effective by making comparisons with other neural network-based methods. BiLSTM gives more than 3% improvement in F1 score compared with the most advanced neural network method. Since feature selection plays an important role in this direction, we combine features to improve the performance. Then we get 87.6% F1 value which outperforms the state-of-the-art method. Moreover, in the cross-domain detection experiment, our method achieves 82.4% F1 value which is about 6% higher than the state-of-the-art method on restaurant domain, and it is also robust on doctor domain.

中文翻译：

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将特征表示加入 BiLSTM 以进行欺骗性评论检测

消费者在购买商品或服务时越来越受到产品评论的影响。同时，欺骗性评论通常会误导用户。在海量评论中手动识别欺骗性评论效率低下且不准确。因此，自动识别欺骗性评论已成为研究趋势。现有的大多数方法都不太有效，因为它们缺乏对评论的深入理解。我们提出了一种具有双向长短期记忆（BiLSTM）和特征组合的神经网络方法来学习欺骗性评论的表示。我们进行了大量实验并证明了我们提出的方法的有效性。具体来说，在混合域检测实验中，通过与其他基于神经网络的方法进行比较，结果证明我们的模型是有效的。与最先进的神经网络方法相比，BiLSTM 在 F1 分数上提高了 3% 以上。由于特征选择在这个方向上起着重要作用，我们结合特征来提高性能。然后我们得到 87.6% 的 F1 值，它优于最先进的方法。此外，在跨域检测实验中，我们的方法实现了 82.4% 的 F1 值，比餐厅领域的 state-of-the-art 方法高约 6%，并且在医生领域也具有鲁棒性。