Book Recommendation Through Sentiment Analysis Using Machine Learning

Madhumita Jha; P. Santhosh Kumar; Aayush Mishra; Sai Sujan Vankadaru

Book Recommendation Through Sentiment Analysis Using Machine Learning

Authors: Madhumita Jha, P. Santhosh Kumar, Aayush Mishra, Sai Sujan Vankadaru

Unique Paper ID: 168628
Volume: 11
Issue: 5
PageNo: 1376-1384

Keywords: Sentiment Analysis Natural Language Processing Book Recommendation User Reviews Machine Learning Personalization Collaborative Filtering Content-Based Filtering.

Abstract:
In today's digital age, where reader preferences are constantly changing, delivering tailored and precise book suggestions is crucial for engaging users and expanding platforms. This study introduces a hybrid book recommendation system that utilizes advanced machine learning methods, such as sentiment analysis and real-time data processing, to tackle the issues faced by traditional recommendation models. The framework emphasizes analyzing user feedback, social media trends, and past reading habits to provide exceptionally personalized and emotionally attuned suggestions. Through the incorporation of predictive modeling, collaborative filtering, and NLP based on deep learning, the system ensures a thorough understanding of user preferences. This adaptable model not only improves recommendation accuracy but also enhances user satisfaction and long-term involvement, offering a holistic solution to the evolving requirements of contemporary e-commerce-driven recommendation platforms.

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ISSN: 2349-6002
Volume: 11
Issue: 5
PageNo: 1376-1384

Book Recommendation Through Sentiment Analysis Using Machine Learning

Available:https://ijirt.org/Article?manuscript=168628

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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