Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm

Abstract

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 (ML) and natural language processing (NLP), 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 (HMM), Constraint-based Viterbi algorithm for POS tagging. By comparing the accuracies, we select the foremost accurate model's result for Sentiment Analysis for determining the character of the sentence.