  Grammar Detection, also referred to as Parts of Speech Tagging of raw text,
is considered an underlying building block of the various Natural Language
Processing pipelines like named entity recognition, question answering, and
sentiment analysis. In short, forgiven a sentence, Parts of Speech tagging is
the task of specifying and tagging each word of a sentence with nouns, verbs,
adjectives, adverbs, and more. Sentiment Analysis may well be a procedure
accustomed to determining if a given sentence's emotional tone is neutral,
positive or negative. To assign polarity scores to the thesis or entities
within phrase, in-text analysis and analytics, machine learning and natural
language processing, approaches are incorporated. This Sentiment Analysis using
POS tagger helps us urge a summary of the broader public over a specific topic.
For this, we are using the Viterbi algorithm, Hidden Markov Model, Constraint
based Viterbi algorithm for POS tagging. By comparing the accuracies, we select
the foremost accurate result of the model for Sentiment Analysis for
determining the character of the sentence.
