EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

TL;DR

This paper introduces EDSA-Ensemble, a novel ensemble architecture combining event detection and sentiment analysis techniques to enhance the accuracy of detecting and understanding the polarity of social media events.

Contribution

The paper presents a new ensemble architecture that integrates multiple machine learning models and text preprocessing methods for improved event sentiment detection.

Findings

Enhanced sentiment classification accuracy over individual models

Effective integration of event detection with sentiment analysis

Utilization of diverse text representations like Word2Vec and Transformers

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.