As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSA-Ensemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models, i.e., raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models.

翻译：随着全球数字化持续发展，技术日益普及且易于使用，社交媒体平台蓬勃发展，成为信息与新闻传播的新途径。围绕当前事件的分享与讨论形成了各类社群。在这些社群中，用户可以就每个事件发表自己的观点。利用情感分析（Sentiment Analysis）理解每个事件相关消息的情感极性以及整个事件的情感倾向，有助于更深入地把握重大趋势的普遍与个体感受，以及在线社交网络的动态变化。在此背景下，我们提出一种新的集成架构——EDSA-Ensemble（事件检测情感分析集成架构），它结合事件检测与情感分析技术，以提高从社交媒体中识别当前事件极性的效果。在事件检测方面，我们采用基于信息扩散的技术，同时考虑时间跨度和主题因素。为检测每个事件的极性，我们对文本进行预处理，并运用多种机器学习和深度学习模型构建集成模型。预处理步骤包括多种词表示模型，即原始词频、TFIDF、Word2Vec和Transformer模型。实验表明，本文提出的EDSA-Ensemble架构在事件情感分类性能上优于单一的机器学习和深度学习模型。