Abstract
Evaluation of emotion and recognition from textual data is a new and important study in the Natural Language Processing (NLP) field that could provide useful information for various applications. Nowadays people prefer sharing their thoughts, feelings, views, emotions, perceptions, etc. on social media in the form of short articles, e-commerce reviews, posts, and comments. It's important to analyze these textual facts in order to comprehend the nature of people’s thoughts or emotions prevailing on social media. Analyzing emotions from textual data is most challenging for researchers due to text data diversity, and restricted word length. This research article experimented popular deep learning algorithms (LSTM, BiLSTM, GRU, BiGRU) for emotion prediction from textual data extracted from social media. Authors found that performance of experimented models is better than traditional machine learning techniques. Authors adopted Ekman's six primary emotions. The performance of these algorithms is measured on accuracy, recall, precision, and F1-score parameters. The results demonstrate that BiGRU outperforms the other approaches in terms of emotion prediction.
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Kumar, P., Babulal, K.S., Mahto, D., Khurshid, Z. (2023). Analyzing Deep Neural Network Algorithms for Recognition of Emotions Using Textual Data. In: Garg, L., et al. Key Digital Trends Shaping the Future of Information and Management Science. ISMS 2022. Lecture Notes in Networks and Systems, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31153-6_6
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