Boukobza Adrien, Burgun Anita, Roudier Bertrand, Tsopra Rosy
Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France.
Inria, HeKA, PariSanté Campus, Paris, France.
JMIR Med Inform. 2022 May 25;10(5):e34306. doi: 10.2196/34306.
Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together.
Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO).
A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81% and a precision of 82% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores.
In reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people.
We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics.
公众参与是缓解大流行的关键要素，深入了解公众舆论有助于鼓励民众成功采用公共卫生措施。在过去几年中，深度学习越来越多地应用于社交网络文本分析。然而，大多数已开发的方法只能单独捕捉主题或情感，而不能同时捕捉两者。
在此，我们旨在开发一种基于深度神经网络的新方法，用于同时捕捉公众话题和情感，并将其应用于世界卫生组织（WHO）宣布新冠疫情后发布的推文。
总共收集了1386496条推文，进行预处理，并按80:20的比例分别划分为训练集和验证集。我们结合词汇表和卷积神经网络来改进情感预测。训练后的模型总体准确率达到81%，精确率为82%，并且能够同时捕捉与预测情感强度得分相关的加权词汇。然后，这些输出通过基于词云表示的交互式可定制网络界面进行可视化。使用词云分析，我们捕捉了极端积极和消极情感强度得分的主要主题。
针对WHO宣布的疫情，推特上讨论了6个负面话题和5个正面话题。推特用户似乎担心国际形势、经济后果和医疗状况。相反，他们似乎对医护人员和社会工作者的奉献以及人们之间的合作感到满意。
我们提出了一种基于深度神经网络的新方法，用于同时从推文中提取公众话题和情感。该方法有助于在大流行等危机期间监测公众舆论。
