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首页> 外文会议>IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability >Sentiment Analysis with CNNs Built on LSTM on Tourists Comments
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Sentiment Analysis with CNNs Built on LSTM on Tourists Comments

机译：在游客评论的LSTM上建立了CNNS的情感分析

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This research developed a sentiment analysis system for customers' comments on a scenic spot. It is based on CNNs built on LSTM for text feature extraction under the deep learning framework. The CNNs built on LSTM model applies convolutional filters of CNNs repeatedly operate on the output matrix of LSTM to obtain robust text feature vector. In the experiments, the optimal parameter configurations for each component of CNNs and LSTM are identified separately in the first place. Then, the entire optimal parameter configuration for the integration recognition frame of the system is identified around the optimum of each component. Experimental results demonstrate that the accuracy for sentiment analysis with CNNs built on LSTM model is improved by 3.13% and 1.71% respectively, compared with a single CNNs or LSTM model.
机译：这项研究为客户对景区的评论制定了一种情感分析系统。它基于在深度学习框架下的文本特征提取的LSTM上建立的CNNS。基于LSTM模型的CNNS在LSTM的输出矩阵上反复运行的CNNS的卷积滤波器来获取强大的文本特征向量。在实验中，首先分别识别CNNS和LSTM的每个组件的最佳参数配置。然后，围绕每个组件的最佳识别系统集成识别帧的整体最佳参数配置。实验结果表明，与LSTM模型内置的CNNS的情绪分析的精度分别提高了3.13％和1.71％，与单个CNNS或LSTM模型相比。

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