基于递归神经网络(RNN)的亚马逊产品评论情感分析比较研究

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引用次数: 0

摘要

本研究解决了亚马逊产品的情感分析问题。实际上,因为意见几乎是所有人类活动的中心,所以情绪分析工具几乎被用于每个经济和社会领域。他们也是我们行为的主要影响者。本文采用递归神经网络(RNN)模型对亚马逊的产品评论进行分类。此外,使用这个特别适合处理顺序数据的模型家族,我们能够从一个初始序列中逐个字符地构建可理解的文本。因此,我们使用三个亚马逊评论数据集来估计作者的态度。结果,我们达到了85%的准确率,与该领域最先进的模型相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative study of Sentiment Analysis on Amazon Product Reviews using Recurrent Neural Network (RNN)
The problem of sentiment analysis on Amazon products is addressed in this research. In reality, because opinions are at the center of practically all human activity, sentiment analysis tools are used in almost every economic and social arena. They are also major influencers of our actions. The recurrent neural network (RNN) model is used to classify the product reviews of Amazon in this paper. Furthermore, using this family of models, which is particularly well-suited to the processing of sequential data, we were able to construct comprehensible text from an initial sequence on a character- by-character basis. As a result, we used three Amazon review datasets to estimate the authors' attitudes. As a result, we achieve results of 85% accuracy, and which are comparable to the greatest state-of-the-art models in this area.
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