Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm

TL;DR

This paper improves sentiment analysis by enhancing the Viterbi algorithm for Part of Speech tagging, which is crucial for understanding sentence structure and emotional tone in NLP tasks.

Contribution

It introduces an improved Viterbi algorithm with constraints for more accurate POS tagging to enhance sentiment analysis accuracy.

Findings

Enhanced POS tagging accuracy over traditional methods

Better sentiment classification results achieved

Comparison shows the improved algorithm outperforms existing models

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 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.