Abstract
Social webs like Instagram, Twitter, and WhatsApp are full of deliberations involving sentiments, feelings, and impressions of human beings worldwide. Moreover, understanding and segregating texts based on emotions is a complex task that could be considered progressive sentiment analysis. As sentiments play a crucial role in human interaction, the skills to perceive it through textual content analysis has numerous applications in natural language processing (NLP) and humancomputer interaction (HCI). This paper suggests classifying and examining tweets based on six basic emotions: happiness, fear, anger, disgust, surprise, and sadness. Language translators are used to apply data augmentation. Experimental results show that augmented data provides better results than the original data.
