Emotion Detection from Micro-Blogs Using Novel Input Representation

The objective of the project is to conduct a comprehensive comparative study of emotion classification from text data using machine learning techniques, focusing on Random Forest-based text analysis and Ensemble Voting methods. Key objectives include exploring various machine learning algorithms, evaluating Random Forest and Ensemble Voting, comparing their performance, identifying the optimal approach, and contributing insights to research and practice in emotion classification. Overall, the project aims to advance the understanding and capabilities of emotion classification from text data for applications such as sentiment analysis and social media analytics.