Sentiment analysis of legal emails using Plutchik's Wheel of Emotions in quantified format

Abstract

Sentiment analysis, which automatically extracts expressions from text, has gained a great deal of analysis attention within the past decade. Sentiment analysis for social networking sites has become an emerging field in text mining, however once we quote email that is wide employed in communication in our everyday tasks analysis into email sentiment analysis isn't to identical proportion. Earlier very less work has been done in extracting emotions from emails. The aim of this paper is to perform sentiment analysis and quantify the emotional intentions expressed in emails and highlighting the dominant one by using Machine learning models like Naïve Bayes, Support vector machine (SVM), RNN (Recurrent Neural Network), Convolution Neural Network (CNN), Word2Vec and comparing the performance of these model. We have classified the emotion into eight totally different classes of the Plutchik’s emotion wheel: joy, trust, fear, surprise, sadness, expectation, anger, and disgust. we have used TF-IDF (Term Frequency Inverse Document Frequency) for feature extraction, to train Naïve-Bayes classifiers and SVM. We have trained all our models on Dens-Dataset and predict emotions from the document passed to that later, which resulted in achieving maximum accuracy using RNN. Dens-Dataset has 10,710 entries containing emotions, which are in Plutchik's wheel.