带有自注意层的噪声正则化双向门控循环单元用于文本和表情符号分类

V. MohanKumarA., N. NandakumarA.
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引用次数: 0

摘要

表情符号能够通过展示视觉情感来表达文本含义之外的情感,使内容更加清晰。最近,表情符号和文本预测变得更加重要,因为很难从数千个候选表情符号中选择合适的表情符号。小型数据集对导致分类的特征描述不佳,并显示出过拟合和欠拟合问题。因此,提出了一种带有自注意层的噪声正则化双向门控循环单元(Bi-GRU)用于文本和表情符号的分类。本文提出的基于方面的情感分析方法Noise regularization Bi-GRU在Twitter数据上进行了一系列的实验来预测tweet的情感。与深度学习模型的准确率86.27%相比,本文提出的基于SAL方法的噪声正则化BGRU的准确率提高了87.77%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noise-Regularized Bidirectional Gated Recurrent Unit With Self-Attention Layer for Text and Emoticon Classification
The emoji are capable of expressing emotion beyond the meaning of the text by displaying visual emotions, which makes the content more distinct. Recently, emoji and text prediction has gained more significance, since it is hard to choose the appropriate one from thousands of emoji candidates. The small-sized dataset provides a poor description of features that resulted in classification and showed overfitting and underfitting problems. Therefore, Noise Regularized Bidirectional Gated Recurrent Unit (Bi-GRU) with Self-Attention Layer (SAL) is proposed for the classification of text and emoji. The proposed Noise Regularized Bi-GRU which is an aspect-based sentiment analysis performs a series of experiments on Twitter data to predict the sentiment of a tweet. The proposed Noise Regularized BGRU with SAL method obtained an accuracy of 87.77 % better when compared to the deep learning model that obtained an accuracy of 86.27 %.
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