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 self-annotation 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 emotion-based 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%.
Highlights
A human newborn comes with primary settings for understanding and communicating basic feelings, as well as an immense ability to learn
With the inclusion of emoticon based features proposed emotion recognition model achieves the highest accuracy of 87.55% and f-score of 65.31% with the use of the REPTree classifier
We addressed the issue of recognizing emotions conveyed in text conversations using traditional machine learning approaches
Summary
A human newborn comes with primary settings for understanding and communicating basic feelings, as well as an immense ability to learn. Sentiment analysis is considered the general task of natural language understanding It is evolved as coarse-grained emotion recognition that is multiclass classification of sentiments. To produce the human like prediction results for the emotion of the conversational text, simple machine learning based models is used with emoticon-based features. Emoticons are important entities conveying emotion expressed by a writer using tiny facial images. To capture the non-verbal emotional clues from the text, which are conveyed by the writer by use of emoji, sentiment information-related resources for emoji are utilized in our www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 12, No 7, 2021 work. A conclusion with possible future directions of this work is described in Section 5 followed by references
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