  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.
