Opinion Text Analysis Using Artificial Intelligence

This paper presents a robust methodology for sentiment analysis of comments leveraging advanced techniques such as Bag of Words BoW , K Nearest Neighbors K NN , Support Vector Machine SVM , and Discriminant Analysis. Sentiment analysis plays a crucial role in understanding user opinions, attitudes, and emotions expressed in textual data. By employing BoVW, we extract discriminative features from comments, capturing both semantic and visual cues. These features are then utilized in conjunction with machine learning algorithms including K NN, SVM, and Discriminant Analysis to classify sentiments accurately. The proposed approach offers a comprehensive framework for sentiment classification, achieving high accuracy and reliability across diverse datasets. Experimental results demonstrate the effectiveness and scalability of the proposed methodology, showcasing its potential for real world applications in sentiment analysis of comments across various domains. Rahul Sagar | Sumit Dalal | Sumiran "Opinion Text Analysis Using Artificial Intelligence" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-3 , June 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64847.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64847/opinion-text-analysis-using-artificial-intelligence/rahul-sagar