Emotion Detection in Textual Data using Deep Learning

Abstract

Technological advancement is something we can no longer ignore, deep learning being one of those advancements. With deep learning, emotions can be extracted and analyzed in textual data to be utilized in various sectors, involving human behavior analysis. The main objective of this paper is to investigate and discover basic emotional categories that are typically exhibited by humans, such as sadness, joy, anger, fear, and disgust. Sectors focused on business can utilize emotion detection for various applications, such as to create personalized services, and in medical scenarios to help develop specialized mental treatments. This paper enables other researchers to enrich their knowledge and develop a more accurate and efficient deep learning model. To create an emotion detection model, analysis regarding the best performing model has to be done. This paper discusses the difference in performance across three different RNN models, namely LSTM, BiLSTM, and GRU. The RNN based models, in theory, share the same architecture and will be evaluated using the ISEAR dataset to create an emotion detection model. The result of this paper concluded that the simplest RNN model, GRU, achieved the highest score across four scoring metrics: accuracy, recall, precision, and F1 score. The GRU model achieved an accuracy of 60.26%, BiLSTM with 59.3%, and LSTM with 57.65%. Hence, based on the results, the GRU-based models are a strong choice for emotion detection when tested on the ISEAR dataset.