Revolutionizing Sentiment Analysis through AI

Revolutionizing Sentiment Analysis through AI

Kankanala L S V S Nagamani Charan, Rasagna T

ABSTRACT:

This study explores the paradigm-shifting impact of Artificial Intelligence (AI) on sentiment analysis, systematically evaluating five prominent machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest, Decision Tree, and Gaussian Naive Bayes (GNB). Through extensive empirical analysis and performance metrics assessment, we discern Random Forest as the most promising and transformative choice for AI-driven sentiment analysis. It consistently exhibits superior accuracy and interpretability across various sentiment analysis tasks, highlighting its potential to revolutionize sentiment analysis within the AI domain.

In addition to technical evaluations, we briefly address ethical considerations tied to AI-driven sentiment analysis, including potential biases and transparency issues inherent to machine learning models. Responsible AI practices and mitigation strategies are emphasized to ensure fair and reliable sentiment analysis outcomes.

In conclusion, this research advocates for the continued integration of Random Forest in AI-powered sentiment analysis systems, offering valuable insights for decision-making, user experience enhancement, and a more profound comprehension of public sentiment. This study contributes to the ongoing discourse surrounding AI's role in sentiment analysis, serving as a practical reference for researchers and practitioners in the field.

Keywords:- KNN,SVC,Random Forest,Decision Tree, and GNB.