Handling 'Happy' vs 'Not Happy': Better sentiment analysis with sentimentr in R

Sentiment Analysis is one of the most obvious things Data Analysts with unlabelled Text data (with no score or no rating) end up doing in an attempt to extract some insights out of it and the same Sentiment analysis is also one of the potential research areas for any NLP (Natural Language Processing) enthusiasts. For an analyst, the same sentiment analysis is a pain in the neck because most of the primitive packages/libraries handling sentiment analysis perform a simple dictionary lookup and calculate a final composite score based on the number of occurrences of positive and negative words. But that often ends up in a lot of false positives, with a very obvious case being'happy' vs'not happy' – Negations, in general Valence Shifters. Consider this sentence: 'I am not very happy'. Any Primitive Sentiment Analysis Algorithm would just flag this sentence positive because of the word'happy' that apparently would appear in the positive dictionary.