An ensemble based method for predicting emotion intensity of tweets

Abstract

Recently, user generated contents have increased tremendously in social media. Twitter is a popular micro-blogging platform in which users share their feelings, opinions, feedback, etc. It has been observed that microblogs are often associated with emotions. Several studies have focused on assigning a given tweet to one of the available emotion categories (e.g., anger, fear, joy, sadness). It is often useful in applications to find the intensity of emotion in the tweets. The focus on identifying emotion intensity is less in the literature. In this paper, we focus on determining the level of emotion intensity in the tweets. We use an ensemble of three methods: Convolution Neural Networks (CNN) with word embedding features, XGBoost with word n-gram and char n-gram features, and Support Vector Regression (SVR) with lexicon and word embedding features. The final prediction of the given tweet is obtained by the average of predictions of individual methods in the ensemble. The performance of ensemble is better than the methods in the ensemble due to diverse features. Our experimental results outperform baseline methods.