Explores real-time sentiment analysis (SA) of Twitter posts in Hindi, adopting a resource-based approach and classifying sentiment on a 3 method scale of negative, positive and neutral. Furthermore, the efficiency of different approaches like part-of-speech (POS) tagging and stop word removal are compared, and ways in which of improving the Hindi Sentiment. We focus on mining sentiments and analyzing them for Hindi language. Hindi is the 4th commonly spoken language in the world. With the increase in the amount of information being communicated via regional languages like Hindi, comes a promising opportunity of mining this information. Mining sentiments in Hindi comes with their share of issues and challenges. Hindi is morphological rich and is a free order language as compared to English, which adds complexity while handling the user-generated content. The scarcity of resources for the Hindi language brings challenges ranging from collection and generation of data-sets. We take up this challenge and work towards building resources- reviews, blogs annotated corpora and subjective lexicon for Hindi language.