A text sentiment analysis model based on self-attention mechanism

L. Ji, Ping Gong, Zhuyu Yao
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引用次数: 2

Abstract

This paper focuses on the problem of text sentiment analysis. The task of sentiment analysis is to extract structured and valuable information from the text data that people express on various platforms. The field of sentiment analysis is attracting more and more attention from researchers. In recent years, due to the continuous development of deep learning theory, there have been many researches on the application of neural network model in sentiment analysis task. Sentiment analysis can be understood as the process of dividing text into different types according to the sentiment information expressed in the text. In this paper, we introduce the self-attention mechanism into sentiment analysis and propose a neural network model based on multi-head self-attention mechanism. In order to better capture the effective information in the text, we combine bidirectional GRU in our model. We evaluate our method on the dataset IMDB (Internet Movie Database). Experimental results show that the accuracy of the proposed model achieves 90.0 on the test dataset. It illuminates that our model outperforms other models. Our model can extract information more effectively in the task of sentiment analysis.
基于自注意机制的文本情感分析模型
本文主要研究文本情感分析问题。情感分析的任务是从人们在各种平台上表达的文本数据中提取结构化的、有价值的信息。情感分析领域越来越受到研究者的关注。近年来,由于深度学习理论的不断发展,人们对神经网络模型在情感分析任务中的应用进行了很多研究。情感分析可以理解为根据文本中表达的情感信息将文本划分为不同类型的过程。本文将自注意机制引入情感分析,提出了一种基于多头自注意机制的神经网络模型。为了更好地捕获文本中的有效信息,我们在模型中结合了双向GRU。我们在数据集IMDB(互联网电影数据库)上评估了我们的方法。实验结果表明,该模型在测试数据集上的准确率达到90.0。这说明我们的模型优于其他模型。该模型可以在情感分析任务中更有效地提取信息。
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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