On the Effect of Prior Knowledge in Text-Based Emotion Recognition

Mohammed A. Almulla
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Mohammed A. Almulla: Computer Science Department – Kuwait University

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 8, 48-58

Abstract: Human emotion recognition has significant importance across various applications, including chatbots, customer support, and airport security. Detecting emotions from text still poses big challenges for both humans and machine learning algorithms. Recognizing emotions from facial expressions or audio recordings tends to yield more accurate results than from textual data. In this paper we suggest a novel approach to text-based emotion recognition by supporting the machine learning algorithm with prior knowledge capabilities. Simply put, we propose combining natural language processing and sentiment analysis to build a database of keywords that are frequently associated with human-specific emotions. This database constitutes the prior knowledge that we use to make predictions about the emotion corresponding a given text. The aim is to enhance prediction accuracy by leveraging a database of keywords with fine granularity. The experimental results of testing this approach confirmed that our algorithm achieved a higher accuracy rate when the prior knowledge was introduced. Initially, the machine learning model achieved recognition accuracy of 99.79% on the training subset and an accuracy rate of 79.02% on the testing subset. With the help of the knowledge-driven database, the accuracy rate of the testing subset became 97.85%, which confirms that prior of keywords associated with emotion classes has a great impact on the performance of the text-based emotion recognition algorithm.

Date: 2024
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