Harnessing Emotive Features for Emotion Recognition from Text

Abstract

With the prevalence of affective computing, emotion recognition becomes vital in any work related to natural language understanding. The inspiration for this work is provided by supplying machines with complete emotional intelligence and integrating them into routine life to satisfy complex human desires and needs. The text being a common communication medium on social media even now, it is important to analyze the emotions expressed in the text which is challenging due to the absence of audio-visual cues. Additionally, the conversational text conveys many emotions through communication contexts. Emoticon serves the purpose of selfannotation of writer's emotion in text. Therefore, a machine learning-based text emotion recognition model using emotive features proposed and evaluated it on the SemEval-2019 dataset. The proposed work involves exploitation of different emotionbased features with classical machine learning classifiers like SVM, Multilayer perceptron, REPTree, and decision tree classifiers. The proposed system performs competitively well in terms of f-score 65.31% and accuracy 87.55%.

An Emotional Analysis Method Based on Multi Model Ensemble Learning

“…Most texts use natural language, and the information contained is unstructured and difficult to be processed by computer. Therefore, how to accurately represent text features is the main factor affecting the performance of text emotion classification [26]. In recent years, researchers have proposed many text representation models such as Boolean model, spatial vector model, latent semantic model and probability model to express the semantics of the text with a specific structure [27].…”