Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Abstract

In the human body, emotion plays a critical function. Emotion is the most significant subject in human-machine interaction. In economic contexts, emotion detection is equally essential. Emotion detection is crucial in making any decision. Several approaches were explored to determine emotion in text. People increasingly use social media to share their views, and researchers strive to decipher emotions from this medium. There has been some work on emotion detection from the text and sentiment analysis. Although some work has been done in which emotion has been recognized, there are many things to improve. There is not much work to detect racism and analysis sentiment on Ukraine -Russia war. We suggested a unique technique in which emotion is identified, and the sentiment is analyzed. We utilized Twitter data to analyze the sentiment of the Ukraine-Russia war. Our system performs better than prior work. The study increases the accuracy of detecting emotion. To identify emotion and racism, we used classical machine learning and the ensemble method. An unsupervised approach and NLP modules were used to analyze sentiment. The goal of the study is to detect emotion and racism and also analyze the sentiment.

Challenges and Opportunities of Text-Based Emotion Detection: A Survey

“…The combination mechanisms include max voting, averaging, weighted averaging, etc. While individual baseline classifiers offer low accuracy and tend to overfit, an ensemble of those base classifiers can reduce overfitting and improve the accuracy [192]. In addition, ensemble learning offers good generalization and robustness [193].…”

“…Maruf et. al [192] used the bagging and boosting ensemble model. Initially, they used the baseline classifier and found that the accuracy was low and overfitting occurred.…”