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A Deep Learning Architecture for Sentiment Analysis

机译:用于情感分析的深度学习架构

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摘要

The fabulous results of Deep Convolution Neural Networks in computer vision and image analysis have recently attracted considerable attention from researchers of other application domains as well. In this paper we present NgramCNN, a neural network architecture we designed for sentiment analysis of long text documents. It uses pretrained word embeddings for dense feature representation and a very simple single-layer classifier. The complexity is encapsulated in feature extraction and selection parts that benefit from the effectiveness of convolution and pooling layers. For evaluation we utilized different kinds of emotional text datasets and achieved an accuracy of 91.2 % accuracy on the popular IMDB movie reviews. NgramCNN is more accurate than similar shallow convolution networks or deeper recurrent networks that were used as baselines. In the future, we intent to generalize the architecture for state of the art results in sentiment analysis of variable-length texts.
机译:深度卷积神经网络在计算机视觉和图像分析中的出色成果最近也吸引了其他应用领域研究人员的关注。在本文中,我们介绍了NgramCNN,这是一种我们设计用于对长文本文档进行情感分析的神经网络体系结构。它使用预训练的词嵌入来实现密集的特征表示和非常简单的单层分类器。复杂性封装在特征提取和选择部分中,这些部分得益于卷积和池化层的有效性。为了进行评估,我们使用了不同种类的情感文本数据集,并且在流行的IMDB电影评论中达到了91.2%的准确性。 NgramCNN比用作基准的类似浅层卷积网络或深层递归网络更准确。将来,我们打算在可变长度文本的情感分析中归纳最新技术的体系结构。

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