Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

Abstract

The issue of the Global Recession is hitting various countries, including Indonesia. Many Indonesians have expressed their opinions on the issue of the global recession in 2023, one of which is from Twitter. By understanding public sentiment, we can assess the impact felt by the public on the issue itself. Sentiment analysis in this research is a form of support to evaluate Indonesia's sustainability in dealing with the issue of Global Recession in accordance with the Sustainable Development Goals (SDGs). However, in previous research, it is still rare to find a model that has good performance in conducting Global Recession Sentiment Analysis. Therefore, the purpose of this research is to propose a machine learning model that is expected to provide good performance in sentiment analysis. The existing sentiment dataset is labeled with the Valence Aware Dictionary for Social Reasoning (VADER) algorithm, then an Ensemble Learning method is designed which is composed of Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms. After that, the Countvectorizer feature extraction with N-Gram, Best Match 25 (BM25), and Word Embedding is carried out to convert sentences in the dataset into numerical vectors so as to improve model performance. The research results provide a more optimal accuracy performance of 95.02% in classifying sentiment. So that the proposed model successfully performs sentiment analysis better than previous research.