This document presents an emotion sensor tool that uses machine learning algorithms and Python libraries to detect emotions in sentences and tweets. The main objective is to analyze emotions like happy, sad, angry, and neutral using techniques like supervised learning, speech recognition, tokenization, and sentiment analysis. The tool analyzes text and speech inputs and classifies emotions using algorithms like logistic regression, linear regression, naive Bayes, and support vector machines. Future areas of improvement include analyzing more complex statements, additional emotions, and improving overall accuracy.