Aiming at the problem that the Aspect-based sentiment analysis in Chinese has low recognition rate due to many steps, this paper proposes an improved BiLSTM-CRF model based on combine the Chinese character vector and Chinese words position feature, which can extract attribute words and sentiment words jointly simultaneously, while extracting Polarity judges of sentiment words. Experiments show that the improved model improves the precision rate by 9.2% 13.32%, recall rate improves 0.48% 21.29%, F-measure improves 7.33% 15.74% compared with Conditional Random Fields (CRF) model and Long Short Term Memory (LSTM) model on the self-built 6357 mobile reviews dataset. The experimental results show that the model improves the accuracy of Aspect-based sentiment analysis and can effectively obtain the information required by users need in evaluation texts.

中文翻译：

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基于BiLSTM-CRF的移动评论的基于方面的中文情感分析

针对中文中基于方面的情感分析由于步骤多而识别率低的问题，提出了一种结合汉字向量和汉字位置特征的改进BiLSTM-CRF模型，可以提取属性词和同时提取情感词，同时提取情感词的极性判断器。实验表明，与条件随机场（CRF）模型和长短期记忆（LSTM）模型相比，改进后的模型将准确率提高了9.2％13.32％，召回率提高了0.48％21.29％，F量度提高了7.33％15.74％自建6357移动评论数据集上。实验结果表明，该模型提高了基于方面的情感分析的准确性，可以有效地获得用户需要的评估文本信息。