基于Novel-CNN的顾客评论情感分析识别

N. Deepa, J. Priya, T. Devi
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引用次数: 1

摘要

情感识别是对情感的识别,它被人工智能、机器学习和深度学习等许多领域所检测到。专业人士需要对商业进行情感分析,如社交媒体监测、品牌监测和客户反馈,这将有助于企业根据从客户那里收集的情感来即兴创作产品。情感分析在商业中用于挖掘客户对产品的感受数据。Facebook、WhatsApp和twitter等现有系统利用情感来感知用户的情绪。在我们提出的系统中,我们使用新颖的卷积神经网络(N-CNN)系统进行情感识别,以便更好地了解客户,从而提高产品质量。为了提高所提出模型的准确性,使用了Amazon对产品销售的评论,该评论可以在kaggle中获得。通过与已有模型的比较,对从多个神经网络中提取的预处理特征进行了识别。基于选择的客户反馈对恒定步骤的过滤、最大池化和必要的激活函数结果的特征实现,并显示出98.3%的准确率,这是比其他机器学习模型更可靠的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentimental Analysis Recognition in Customer review using Novel-CNN
Sentimental Emotion Recognition is the recognition of emotion which is detected by many fields such as Artificial intelligence, Machine Learning and Deep Learning. The professionals need sentimental analysis for business like social media monitoring, brand monitoring and customer feedback which will help in business for improvising the product based on the emotion gathered from the customers. Sentimental analysis is used in business to mine the data of customers about how they are feeling about the product. Existing systems like Facebook, WhatsApp and twitter use sentimental emotions for sensing the user’s emotions. In our proposed system we are using the Novel Convolution Neural Network (N-CNN) system for sentimental recognition to make better understanding of customers which can be used to improve the product quality. To enhance the accuracy of the proposed model Amazon review for product sales is used which is available in kaggle. Using the proposed model the model in comparison with existing model the preprocessed feature which are extracted from multiple neural networks are recognized. Feature based on the selected customer feedback on constant steps of filtering, max pooling and necessary activation function results are implemented and shown 98.3% of accuracy which is more reliable results than the other Machine learning model.
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