Emotions are a necessary and fundamental component of our existence. Everything we say, do, or do not say, and body language exhibit some of our emotions, albeit not immediately. To understand a person's most basic behaviour, we must examine their emotions using emotional data, also known as affect data. For this purpose speech, text, facial expressions, and other data types may be used. Affective (Emotional) Computing, which analyses emotions using emotional data, is an interdisciplinary field. Emotion recognition from text is a challenging task in the field of natural language processing (NLP). While there has been great development, there is still potential for improvement. This analysis is used in a variety of applications to understand human thoughts based on their conversation. Deep learning advances have greatly improved algorithms' capability to understand text. Text classification into several groups is now being researched extensively. However, the application of these researches is very limited in local and regional languages such as Hindi. This study will focus on text emotion analysis, specifically for the Hindi language.
Emotion Recognition, Text Analysis, Machine learning, Deep learning, BERT Transformer