A Review on Sentiment Analysis Based Top Organizations Rating Using Machine Learning

Umesh Narayan; Devendra Kumar

A Review on Sentiment Analysis Based Top Organizations Rating Using Machine Learning

Authors: Umesh Narayan, Devendra Kumar

Unique Paper ID: 166369
Volume: 11
Issue: 2
PageNo: 539-545

Keywords: Sentiment Analysis Support Vector Machines (SVM) Naive Bayes Random Forest.

Abstract:
In the era of digital transformation, sentiment analysis has emerged as a powerful tool to interpret and analyze user opinions across various platforms. This review paper focuses on the application of sentiment analysis for rating top organizations using machine learning techniques. We explore the methodologies employed in sentiment analysis, including data collection, preprocessing, feature extraction, and the implementation of machine learning algorithms. Various machine learning models such as Support Vector Machines (SVM), Naive Bayes, Random Forest, and neural networks are evaluated for their efficacy in sentiment classification. Additionally, we discuss the role of advanced techniques like deep learning and natural language processing in enhancing the accuracy of sentiment analysis. The paper also highlights the challenges faced in sentiment analysis, including handling sarcasm, context understanding, and language diversity. Through a comprehensive examination of recent studies and their findings, this review provides insights into the current trends and future directions in sentiment analysis for organizational rating, emphasizing the importance of accurate sentiment interpretation in decision-making processes.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 2
PageNo: 539-545

A Review on Sentiment Analysis Based Top Organizations Rating Using Machine Learning

Available:https://ijirt.org/Article?manuscript=166369

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