Emotion Detection Analysis using Text through Machine Learning Techniques

Abstract

Text is the most comprehensive collection of human knowledge accumulated over thousands of years. If this knowledge is explored for deeper insights, it will have even greater importance. Emotions are the abstract thing which can explains or display actions of humans. High Active Rate on social media
platforms such as Twitter, Instagram, Reddit creates a huge data for analysis and research purpose. Everybody likes to express their feeling through these platforms in form of texts, images and videos. But text is the most used form of expression. Various approaches are used for the analysis purpose. In
this paper we have performed Emotion Detection Analysis. Emotion Detection is a machine learning method that has been around for a long time. We performed the emotion detection on the dataset taken from the Github to show how important insights may be gleaned from a large amount of textual data obtained from the internet. The data is preprocessed in small steps such as the tokenization, textcleaning normalization, feature scaling and after that data is fed into the machine learning models. After the process of choosing the model, the data is trained and tested and results and the conclusion are made. These conclusions are reached using traditional machine learning algorithms: Multinomial Naive Bayes (NB) Classifier, Logistic Regression (LR), K-Nearest Neighbor method (KNN). In
addition, the outcomes of these algorithms were compared using the confusion matrix, accuracy is calculated and compared using different classifier and most used word among the data was figured out from the analysis.