Sentiment analysis has started gaining thrust for languages other than English in recent times, but analyzing emotions for these languages has not been able to draw much attention from researchers. Today’s era witness people being stressed in their day-to-day life owing to family, health, work, or wealth issues and use social media as a medium to express this psychological burden. In this proposed work, we aim to measure the strength of emotions expressed through text for Hindi language. Detecting correct emotion from natural language text is a critical issue. The classification is performed by creating a lexicon of Hindi polar words and a dataset of co-occurring words which generally tend to occur together in sentences. The polarity of Hindi text is determined, and emotion class identified, and the strength of underlying emotion is measured. Empirical results show that the test sentences are correctly classified into their emotion class and the strength of emotion correctly measured. This work will in the future be integrated with chatbots to measure the level of stress, anger and fear experienced by people in today’s time and thus they may be counseled accordingly to avoid any dire alarms.