Performance Improvement Using CNN for Sentiment Analysis

Moch. Ari Nasichuddin, T. B. Adji, Widyawan Widyawan
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引用次数: 9

Abstract

The approach using Deep Learning method provides great results in various field implementations, especially in the field of Sentiment Analysis. One of Deep Learning methods is CNN which has the ability to provide great accuracy in some previous research. However, there are some parts of the training process which can be improved to upgrade the accuracy level and the training time. In this paper, we try to improve the accuracy and processing time of sentiment analysis using CNN model. By tuning the filter size, frameworks, and pre-training, the results show that the use of smaller filter size and pre-training word2vec provide greater accuracy than some previous studies.
使用CNN进行情感分析的性能改进
使用深度学习方法的方法在各个领域的实现中提供了很好的结果,特别是在情感分析领域。深度学习方法之一是CNN,它在以前的一些研究中能够提供很高的准确性。然而,训练过程中有一些部分可以改进,以提高精度水平和训练时间。在本文中,我们尝试使用CNN模型来提高情感分析的准确性和处理时间。通过调整过滤器大小、框架和预训练,结果表明,使用较小的过滤器大小和预训练word2vec比以前的一些研究提供了更高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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