Detecting Human Moods using Self WhatsApp Chat Data with Natural Language Tool Kit (NLTK)

Humans are bounded with emotions expressed in the form of words as a syntax to their languages. It is itself a debate that how can a machine represent emotions with the words delivered through it. A syntax without expressible emotions may lead to generate misunderstanding followed by skirmishes even among the most cited relation, The Marriage. Technologist are putting efforts to redefine the transmission able messages with emotions like emoji's, can be different from the context. Sentiment analysis is a science term in a domain of cognitive science to analyze and deduce the emotions behind the words non-invasively. To date, several off-the-shelf tools are freely available for classifying the sentiment polarity of an input text that is its positive, negative, or neutral semantic orientation. However, in this project, we proposed a method to analyze sentiment from a chat text. Pursing to the goal, we performed experiments on WhatsApp chat or a review based analysis for Business Whatsapp chat concurrently. Business data analytics might bring insights by learning from the customer's cognitive prospect of thinking. Our results are evident of the truth behind the text compared to the index created with a quantitative analysis manually. Our contribution is to predict the reaction of an individual towards a topic either positive or negative.