Recognizing emotions in text using ensemble of classifiers

Abstract

Emotions constitute a key factor in human nature and behavior. The most common way for people to express their opinions, thoughts and communicate with each other is via written text. In this paper, we present a sentiment analysis system for automatic recognition of emotions in text, using an ensemble of classifiers. The designed ensemble classifier schema is based on the notion of combining knowledge-based and statistical machine learning classification methods aiming to benefit from their merits and minimize their drawbacks. The ensemble schema is based on three classifiers; two are statistical (a Naïve Bayes and a Maximum Entropy learner) and the third one is a knowledge-based tool performing deep analysis of the natural language sentences. The knowledge-based tool analyzes the sentence׳s text structure and dependencies and implements a keyword-based approach, where the emotional state of a sentence is derived from the emotional affinity of the sentence’s emotional parts. The ensemble classifier schema has been extensively evaluated on various forms of text such as, news headlines, articles and social media posts. The experimental results indicate quite satisfactory performance regarding the ability to recognize emotion presence in text and also to identify the polarity of the emotions.