Deep Learning-Based Hybrid Recommendation System with NLP- HAEC-Based Sentiment Analysis
Keywords:
Hybrid Recommendation System, natural language processing, Hybrid Agglomerative Elbow Clustering, Deep Learning Convolutional Neural NetworkAbstract
Customer feedback analysis is pivotal in improving products and services, enhancing customer satisfaction, and enabling personalized recommendations. The exponential growth of customer feedback data necessitates automated techniques for efficient analysis. However, the conventional methods failed to provide the perfect recommendations to the users due to cold-start problems of user interactions. So, this research aims to leverage the Hybrid Recommendation System Network (HRS-Net) using deep learning and natural language processing (NLP) techniques to analyse customer feedback. Initially, NLP-based Hybrid Agglomerative Elbow Clustering (HAEC) is introduced to cluster the user data and items separately based on multilevel user-item interactions. Then, the Convolutional Recurrent Neural Network (CRNN) is trained with the HAEC features, which learns the user feedback based on its textual features. Finally, the CRNN provides accurate suggestions to the users based on the pre-trained HAEC interactions. Finally, the simulation results show that the proposed method resulted in superior performance compared to traditional machine learning, clustering, and filtering-based recommendation systems. The proposed HRS-Net achieved 98.54% accuracy, 98.38% precision, 98.63% recall, and 98.45% F1-score, which are higher than traditional approaches.
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Krosuri, Lakshmi Revathi, and Rama Satish Aravapalli. "Novel heuristic bidirectional-recurrent neural network framework for multiclass sentiment analysis classification using coot optimization." Multimedia Tools and Applications (2023): 1-21.
Sharma, S. P., Singh, L., & Tiwari, R. (2023). Integrated feature engineering-based deep learning model for predicting customer's review helpfulness. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-18.
Farhat, S., Abdelkader, M., Meddeb-Makhlouf, A., & Zarai, F. (2023). CADS-ML/DL: efficient cloud-based multi-attack detection system. International Journal of Information Security, 1-25.
Bountakas, Panagiotis, et al. "Defense strategies for Adversarial Machine Learning: A survey." Computer Science Review 49 (2023): 100573.
Hiriyannaiah, Srinidhi, Siddesh GM, and K. G. Srinivasa. "DeepLSGR: Neural collaborative filtering for recommendation systems in smart community." Multimedia Tools and Applications 82.6 (2023): 8709-8728.
Shanthini, B., and N. Subalakshmi. "Advanced Data Mining Enabled Robust Sentiment Analysis on E-Commerce Product Reviews and Recommendation Model." 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS). IEEE, 2023.
Jabeur, Sami Ben, et al. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research." Journal of Business Research 158 (2023): 113631.
Daniel, D. A. J., & Meena, M. J. (2023). Deep learning-based hybrid sentiment analysis with feature selection using an optimization algorithm. Multimedia Tools and Applications, 1-24.
Parmar, J., Chouhan, S., Raychoudhury, V., & Rathore, S. (2023). Open-world machine learning: applications, challenges, and opportunities. ACM Computing Surveys, 55(10), 1-37.
Chauhan, G. S., Nahta, R., Meena, Y. K., & Gopalani, D. (2023). Aspect-based sentiment analysis using deep learning approaches: A survey. Computer Science Review, 49, 100576.
Khan, W., Daud, A., Khan, K., Muhammad, S., & Haq, R. (2023). Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends. Natural Language Processing Journal, 100026.
Chan, Kit Yan, et al. "Deep Neural Networks in the Cloud: Review, Applications, Challenges and Research Directions." Neurocomputing (2023): 126327.
Iftikhar, Saman, et al. "Amazon products reviews classification based on machine learning, deep learning methods and BERT." TELKOMNIKA (Telecommunication Computing Electronics and Control) 21.5 (2023): 1084-1101.
Amirifar, Tolou, Salim Lahmiri, and Masoumeh Kazemi Zanjani. "An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features." IEEE Transactions on Computational Social Systems (2023).
Karabila, Ikram, et al. "Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis." Future Internet 15.7 (2023): 235.
Sangeetha, J., and U. Kumaran. "Sentiment analysis of amazon user reviews using a hybrid approach." Measurement: Sensors 27 (2023): 100790.
Deshai, N., and B. Bhaskara Rao. "Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping." Soft Computing 27.14 (2023): 9861-9875.
Ullah, Tahir, et al. "Exploring and mining rationale information for low-rating software applications." Soft Computing (2023): 1-26.
Manikandan, B., P. Rama, and S. Chakaravarthi. "Original Research Article An automatic product recommendation system in e-commerce using Flamingo Search Optimizer and Fuzzy Temporal Multi Neural Classifier." Journal of Autonomous Intelligence 6.2 (2023).
Balaji, Penubaka, and D. Haritha. "An Ensemble Multi-Layered Sentiment Analysis Model (EMLSA) for Classifying the Complex Datasets." International Journal of Advanced Computer Science and Applications 14.3 (2023).
Duma, Ramadhani Ally, et al. "DHMFRD–TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings." Multimedia Tools and Applications (2023): 1-17.
Elangovan, Durai, and Varatharaj Subedha. "Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews." Engineering, Technology & Applied Science Research 13.3 (2023): 10989-10993.
Qayyum, Huma, et al. "FRD-LSTM: a novel technique for fake reviews detection using DCWR with the Bi-LSTM method." Multimedia Tools and Applications (2023): 1-15.
Gheewala, Shivangi, et al. "Exploiting Deep Transformer Models in Textual Review Based Recommender Systems." Expert Systems with Applications (2023): 121120.
Choudhary, Chaitali, Inder Singh, and Manoj Kumar. "SARWAS: Deep ensemble learning techniques for sentiment based recommendation system." Expert Systems with Applications 216 (2023): 119420.
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