Deep Learning-Based Sentiment-Aware Product Recommendation System Using Hybrid CNN-BiLSTM Networks

Registration ID: IJNRD_327351

Published ID: IJNRD2607343

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Ms. Ketaki A. Ghatage, Mr. Ravindra S. Kamble

Product Recommendation, Sentiment Analysis, Deep Learning, CNN, Bi-LSTM, Hybrid Learning Model, Natural Language Processing (NLP), Opinion Mining, Review Analytics, Text Classification, Emotion Recognition, Personalized Recommendation, Feature Learning, E-Commerce Intelligence, Recommendation Systems.

There is an unprecedented amount of user content generated these days, including product reviews and ratings, and text feedback, due to the rise of ecommerce sites. These reviews offer great insight into what consumers loved, hated and were pleased with. Traditional recommender systems tend to depend on explicit feedback signals, such as ratings, or purchase histories, which do not necessarily capture the sentiment and feelings of customer reviews/feedback. To overcome this, the following research, DL Based Product Recommendation System Using Comment Sentiment Analysis is proposed. The proposed framework employed advanced Natural Language Processing (NLP) methods along with the incorporation of CNN along with a Bidirectional Long Short-Term Memory ( Bi-LSTM) network for effective provision of semantic representation and contextual sentiment information of textual reviews. The model classification and sentiment classification segments all customer feedback into positive, negative or neutral sentiment classes; in addition, it uses the sentiment information gathered during the classification and sentiment classification process for the recommendation process as a basis for improving personalization and recommendation results. CNN-BiLSTM is hybrid CNN-Bi-LSTM feature extraction structure to capture patterns and long-range dependence from review texts. This integrated learning technique has a impact on the sentiment classification accuracy, and gives a better result to customer sentiments prediction. so, the sentiment-based recommendation system can provide better match the user's preferences and emotional reaction. so studies show that sentiment-based features can enhance the relevance and effectiveness of recommendation compared to the traditional recommendation methods that work on collaborative filtering techniques. The proposed framework is intelligent and adaptive development that provides accurate, meaningful and user-centric product recommendations in the contemporary e-commerce context. Therefore, the system offers significant advantages to consumers and businesses in terms of customer satisfaction, decision-making, and personalization strategies.

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Ms. Ketaki A. Ghatage & Mr. Ravindra S. Kamble (July-2026). Deep Learning-Based Sentiment-Aware Product Recommendation System Using Hybrid CNN-BiLSTM Networks. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(7), d405-d416. https://ijnrd.org/papers/IJNRD2607343.pdf

Volume 11 Issue 7, July-2026

Pages : d405-d416

Paper Reg. ID: IJNRD_327351

Published Paper Id: IJNRD2607343

Research Area: Other area not in list

Author Type: Indian Author

Country: Kolhapur , Maharashtra , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2607343.pdf

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