Emotion recognition is a critical sub-field within Affective Computing and a step towards comprehensive natural language understanding. This might pave the way for a wide range of applications in natural language processing for social good, such as suicide prevention and employee mood detection as well as helping businesses provide personalized services to their users. Due to a scarcity of resources and a lack of standard labeled textual corpora for emotions in Hindi, constructing an emotion analysis system is a tough task in such a low resource language. This paper presents a novel multilabel Hindi text dataset of 58000 text samples with 28 emotion labels and a fine-tuned Multilingual BERT-based transformer model on the fine-grained dataset. The model achieves state of the art performance with an overall ROC-AUC score of 0.92 upon evaluation.