Opinion mining plays an important role in public opinion monitoring, commodity evaluation, government governance, and other areas. One of the basic tasks of opinion mining is to extract the expression elements, which can be further divided into direct subjective expression and expressive subjective expression. For the task of subjective expression extraction, the methods based on neural network can learn features automatically without exhaustive feature engineering and have been proved to be efficient for opinion mining. Constructing adequate input vector which can encode sufficient information is a challenge of neural network-based approach. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed. Then, we use neural network and conditional random field to train and predict the expressions and carry out comparative experiments on different methods and features combinations. Experimental results show the performance of the proposed model, and the F value outperforms other methods in comparative experimental dataset. Our work can provide hint for further research on opinion expression extraction.

中文翻译：

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社会舆论挖掘的主观表情提取方法

意见挖掘在舆论监督，商品评估，政府治理和其他领域中发挥着重要作用。观点挖掘的基本任务之一是提取表达元素，可以将其进一步分为直接主观表达和表达主观表达。对于主观表情提取的任务，基于神经网络的方法可以自动学习特征，而无需详尽的特征工程，并且已经证明是有效的观点挖掘方法。构造可编码足够信息的足够输入向量是基于神经网络方法的挑战。为了解决这个问题，提出了一种将不同特征与词向量相结合的新颖表示方法。然后，我们使用神经网络和条件随机场来训练和预测表达式，并针对不同的方法和特征组合进行比较实验。实验结果表明了该模型的性能，并且在对比实验数据集中，F值优于其他方法。我们的工作可以为意见表达的进一步研究提供线索。