[论文解读] Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks

Bahareh Golchin, Noushin Riahi|ArXiv.org|Mar 26, 2025

Sentiment Analysis and Opinion Mining被引用 4

一句话总结

本文提出一个监督式深度学习框架，将双向LSTM和CNN结合起来，将Twitter消息分类为四种情感类别，准确率约为93%。

ABSTRACT

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

研究动机与目标

• 需要在商业和组织洞察方面自动化情感与情绪分析，以应对社交媒体数据的需求。
• 利用大规模Twitter数据，在主题和事件中对情绪状态进行分类。
• 提出一个结合双向LSTM和CNN的深度学习框架，以提升情感分类性能。

提出的方法

• 收集各种主题的Twitter数据，以创建带标签的情感状态数据。
• 对推文应用双向长短期记忆（BiLSTM）和卷积神经网络（CNN）的组合。
• 使用监督学习对混合模型进行多类情感分类训练。
• 评估模型并与先前工作进行性能对比；报告平均准确率。
• 说明深度学习方法通过大规模数据提升学习能力，从而获益。

实验结果

研究问题

• RQ1 混合BiLSTM-CNN模型能否有效地将Twitter消息分类为四种情感类别？
• RQ2 所提出的框架在Twitter数据上的情感分类是否优于先前的方法？
• RQ3 在所使用的数据集上，BiLSTM-CNN方法能达到的可实现准确率是多少？
• RQ4 该方法如何利用深度学习来提升情感检测，相较于传统方法？

主要发现

• 所提出的框架将推文分类为四种情感类别，平均准确率为93%。
• 结果显示与作者声称的前人工作相比，准确率有所提升。
• 深度学习方法利用大型Twitter数据集来提升情感检测的学习能力。