Enhanced Sentiment Analysis of Customer Reviews
Year: 2025
Type: article
Abstract: The growing reliance on customer feedback for improving products and services has made sentiment analysis acrucial task for businesses. This study focuses on enhancing sentiment analysis using advanced machinelearning and deep learning models. The system combines Random Forest, XGBoost, LSTM, CatBoost, andBERT—leveraging Hugging Face’s Transformers library. Random Forest and XGBoost offer strong ensemblelearning capabilities, LSTM captures long-term dependencies in text, CatBoost handles categorical variablesefficiently, and BERT introduces bidirectional context-aware representations of text. With BERT’s powerfultransformer-based embeddings, the model improves its ability to interpret nuanced and complex sentiments,including sarcasm and idioms. The integration of these diverse models aims to deliver a highly accurate, robust,and scalable sentiment analysis framework, enabling businesses to gain actionable insights and enhancecustomer satisfaction.
Authors: Jammula Vijayalakshmi, Jampu Abhinaya, Jonnalagadda Subhashini, Mrs. Zareena Noorbasha
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6949532998
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