用双向LSTM与卷积网络结合，93%准确率识别推特情绪。
Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks
- 融合双向LSTM与卷积网络捕捉时序与局部特征。
- 在多主题推特数据上实现平均93%分类准确率。
- 适合需实时分析社交媒体情绪的商业与舆情研究者。
近年来，识别社交媒体文本中的情感与情绪成为重要议题。通过分析情感与情绪，可获知人们对产品、服务、组织、人物、话题及事件的观点、感受与态度。在现实世界中，企业和组织亟需工具收集公众对其产品、服务或活动的情感反馈。本文利用拥有约4.2亿活跃用户的推特社交网络进行数据采集，用户在此分享对个人事务、政策、产品、事件等的看法。由于数据丰富，适合用于情绪状态的分类。本研究采用监督学习与深度神经网络算法，对推特用户情绪状态进行分类。利用深度学习提升模型学习能力，应对海量数据。通过结合双向长短期记忆网络（Bi-LSTM）与卷积神经网络（CNN），将收集的多主题推文分为四类情绪类别。实验结果显示，该框架平均准确率达93%，优于以往工作。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。