[论文解读] A CNN-LSTM-based hybrid deep learning approach to detect sentiment polarities on Monkeypox tweets

Krishna Kumar Mohbey, Gaurav Meena+2 more|arXiv (Cornell University)|Aug 25, 2022

Poxvirus research and outbreaksImmunology and Microbiology被引用 18

一句话总结

本研究提出了一种CNN-LSTM混合深度学习模型，用于对与猴痘相关的推文进行情感极性分类——包括正面、负面和中性。通过利用卷积层提取局部特征，LSTM建模序列上下文，该模型在真实推文数据集上实现了94%的准确率，优于传统机器学习方法，提升了在公共卫生危机期间对公众情感的理解。

ABSTRACT

People have recently begun communicating their thoughts and viewpoints through user-generated multimedia material on social networking websites. This information can be images, text, videos, or audio. Recent years have seen a rise in the frequency of occurrence of this pattern. Twitter is one of the most extensively utilized social media sites, and it is also one of the finest locations to get a sense of how people feel about events that are linked to the Monkeypox sickness. This is because tweets on Twitter are shortened and often updated, both of which contribute to the platform's character. The fundamental objective of this study is to get a deeper comprehension of the diverse range of reactions people have in response to the presence of this condition. This study focuses on finding out what individuals think about monkeypox illnesses, which presents a hybrid technique based on CNN and LSTM. We have considered all three possible polarities of a user's tweet: positive, negative, and neutral. An architecture built on CNN and LSTM is utilized to determine how accurate the prediction models are. The recommended model's accuracy was 94% on the monkeypox tweet dataset. Other performance metrics such as accuracy, recall, and F1-score were utilized to test our models and results in the most time and resource-effective manner. The findings are then compared to more traditional approaches to machine learning. The findings of this research contribute to an increased awareness of the monkeypox infection in the general population.

研究动机与目标

• 利用来自Twitter的用户生成内容分析公众对猴痘的情感。
• 开发一种混合深度学习模型，能够对三种情感极性（正面、负面和中性）进行分类。
• 评估该模型在情感检测方面相对于传统机器学习方法的性能。
• 通过计算分析揭示社会对猴痘爆发的反应，从而为公共卫生意识做出贡献。

提出的方法

• 采用混合深度学习架构，利用卷积神经网络（CNN）从推文文本中提取局部n-gram特征。
• 使用长短期记忆（LSTM）网络对推文序列中的序列依赖关系和长距离上下文进行建模。
• 应用词嵌入将文本数据转换为密集向量表示，作为深度学习模型的输入。
• 使用分类交叉熵损失和Adam优化算法进行端到端训练，并采用早停策略。
• 使用标准指标（准确率、召回率和F1-score）在所有三个情感类别上评估模型性能。
• 将该方法与经典机器学习模型（如SVM、朴素贝叶斯和逻辑回归）在相同数据集上的表现进行比较。

实验结果

研究问题

• RQ1 CNN-LSTM混合模型在真实世界猴痘相关推文的情感极性分类中效果如何？
• RQ2 所提出的深度学习模型与传统机器学习方法在情感分类中的表现相比如何？
• RQ3 该模型在正面、负面和中性情感类别上的准确率、召回率和F1-score有何差异？
• RQ4 该混合架构在捕捉短文本社交媒体内容中的局部文本模式和序列上下文方面达到何种程度？

主要发现

• 所提出的CNN-LSTM模型在猴痘推文情感分类任务中取得了94%的测试准确率。
• 该模型在所有三个情感类别上均表现出较强的F1-score，表明其在正面、负面和中性推文上的性能均衡。
• CNN-LSTM模型在准确率和F1-score方面均优于传统的机器学习模型，如SVM、朴素贝叶斯和逻辑回归。
• 使用词嵌入和通过LSTM进行序列建模显著提升了模型捕捉短文本中细微情感的能力。
• 结果证实，混合深度学习模型非常适合在公共卫生事件期间对社交媒体数据进行情感分析。