Deep Learning for Text Based Emotion Classification from Social Media

Affective Computing is the study of systems that can recognize underlying human emotions. To be able to detect this, the systems usually have a sensor that captures the features of the input to evaluate it further. Based on this idea, we tried to explore several state-of-art machine learning methods and deep learning methods that are used to classify emotions. However, recognizing emotions is relatively a daunting task for an un-social computer. There are several techniques to model the emotions from text, some implement machine learning, others take advantage of the deep learning technology. Therefore, we evaluated several models of Machine Learning methods and recent Deep Learning methods to make a comparison with previous related works. The dataset used in this research was from social media. Recognizing emotions from social media provides some non-verbal insights for the readers. The experiment demonstrates that deep learning models outperform several previous results and machine learning models on emotion recognition tasks. The results demonstrate that the model trained with BERT achieved the accuracy of 93.75%. Moreover, Love and Joy class is relatively challenging to distinguish.