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混合深度学习方法用于使用文本和表情符号进行情感分析

Arjun Kuruva¹, C Nagaraju Chiluka¹

¹Department of Computer Science and Engineering, YSR Engineering College of Yogi Vemana University, Proddatur, Andhra Pradesh, India.

Network (Bristol, England)

|May 29, 2024

概括

这项研究引入了一种新的情绪分析 (SA) 模型,该模型包含文本和表情符号. 采用混合分类器的电鱼定制鱼气味优化 (ECSSO) 改进的模型显示,与现有方法相比,准确度显著提高.

科学领域:

自然语言处理自然语言处理.
机器学习机器学习
人工智能的人工智能

背景情况:

情感分析 (SA) 根据意见极性对文本进行分类.
现有的SA模型经常在微妙的情感表达方面扎,特别是在涉及表情符号时.
整合表情符号为提高SA准确性提供了一个独特的挑战和机会.

研究的目的:

开发和评估一种新的情绪分析模型,有效地整合文本数据和表情符号.
增强特征提取和分类技术,以改善情绪极性检测.
引入和应用一个新的优化算法,电鱼定制鱼气味优化 (ECSSO),用于模型性能调整.

主要方法:

将数据预处理为纯文本格式和带表情符号的文本格式.
使用N-grams,修改的术语频率-反向文档频率 (TF-IDF) 和字袋 (BoW) 的特征提取.
使用常规神经网络 (CNN) 和多层感知 (MLP) 来进行表情符号增强的SA分类,CNN权重由ECSSO优化.
基于文本的SA的混合长短期记忆 (LSTM) 和循环神经网络 (RNN) 分类器,LSTM权重由ECSSO优化.

主要成果:

拟议的模型表现出优异的特异性,在70%的门下,与其他模型相比,实现了29.01%至42.75%的改进.
ECSSO算法有效地优化了CNN和LSTM模型的权重.
混合LSTM-RNN方法与ECSSO相结合,为基于文本的情绪分析提供了一个强大的机制.

结论:

整合表情符号显著提高了情绪分析模型的性能.
ECSSO算法是SA的深度学习模型的一个有前途的优化技术.
开发的混合模型为情绪分析提供了更准确,更有效的方法,优于现有方法.

关键词:

情绪分析是一种情绪分析.这是分类分类的分类.特性提取特性提取优化的优化优化优化.代币化的代币化.

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