SENTIMENT ANALYSIS AND EMOTION DETECTION FROM TEXT

Abstract

Detecting emotions and thoughts in text is a fascinating topic in machine learning for natural language processing. Emotions and feelings can often be revealed through specific phrases. Many people around the world use foreign languages, and many documents are written in English. Some individuals do not always use proper punctuation in their texts. Unlike other methods, we study emotions in text with or without punctuation to explore how to design an effective emotional management system using certain beneficial approaches. By developing our methods in a specific way, we can better track and identify feelings more accurately. In this paper, we applied various techniques to identify emotions, including Naïve Bayes classifier, linear SVM, logistic regression, and random forest. Among these, random forest achieved the highest accuracy. The challenge addressed in this paper is recognizing emotions or feelings that are not explicitly shared in posts, blogs, and social media pages using this advanced learning algorithm. The goal of sentiment analysis is to understand people's overall attitudes within a community. The Internet is a place where people from around the world share their opinions on various topics. They discuss everyday issues, complain about products, and give positive or negative feedback. This makes the Internet a valuable source of information for opinion mining and sentiment analysis. In computer science, sentiment analysis has many uses, such as identifying thoughts that can improve customer messaging and understanding opinions in written emails. However, well-written data is often limited to a few expressions, which can reduce the chances of accurate analysis. The authors have collected data from text messages to address these issues. The main aim of the project is to provide a clear written statement that conveys emotion. The proposed model uses two strategies to determine which one works best. The biggest challenge in sentiment analysis is interpreting the text according to the underlying thoughts.