Emotion Detection and Analysis using Textual Data through Trainable and Pre-trained Word Embedding Methods

Emotion expression modes play a significant role in human communication. Humans use emotions to convey their state of mind to each other on platforms such as X (formerly Twitter), Facebook, and other online social networks. People often express their emotions using free text, which triggers a vast research area of emotion detection and analysis. This work aims to detect and analyze emotions from unstructured text data. For this purpose, this research study proposes a solution to the problem by building a deep artificial neural network model using trainable and pre-trained word embedding methods. Afterward, the performance of the models developed with different word embeddings is evaluated using the performance metrics. Experimental works demonstrate that the deep artificial neural network with trainable word embedding surpassed all other models by achieving 67.36% accuracy, 53.27% recall, 82.62% precision, and 64.50% F-measure.