Abstract
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.
Abstract (translated)
随着全球数字技术的发展,技术变得更加 affordable 和易用,社交媒体平台也蓬勃发展,成为传播信息和新闻的新途径。社区围绕着分享和讨论当前事件而建立。在这些社区中,用户可以分享他们对每个事件的看法。使用Sentiment Analysis来理解每个事件属于某个事件的信息和情感极性,以及整个事件的信息和情感极性,可以帮助更好地理解重要趋势和在线社交网络的动态。在这种情况下,我们提出了一个新的集成架构,EDSA-Ensemble(事件检测情感分析集成),它使用事件检测和情感分析来提高从社交媒体中检测当前事件的信息和情感极性。对于事件检测,我们使用基于信息扩散的技术,考虑到时间跨度和话题。为了检测每个事件的情感极性,我们预处理文本,并使用多个机器和深度学习模型创建一个集成模型。预处理步骤包括几个词表示模型,例如原始频率、TFIDF、Word2Vec和Transformer。提议的EDSA-Ensemble架构比单个机器和深度学习模型提高了事件情感分类的性能。
URL
https://arxiv.org/abs/2301.12805
