A smart recommendation model for airline reviews via deep learning sentiment analysis (text ratings fusion with LSTM)

NSAR-T: A Fine-Grained Aspect-Based Sentiment and Transformer Framework for Personalized Recommendations

A Nuanced Sentiment-Aware Recommendation with Transformers (NSAR-T) model incorporating Aspect-Based Sentiment Analysis (ABSA) with the Robustly Optimized Bidirectional Encoder Representation from Transformers (RoBERTa) to enable the generation of more granular personalized recommendations.

Sentiment Analysis on E-commerce Product Reviews using Deep Learning

The proposed model provides an efficient solution for automating sentiment classification in large-scale e-commerce platforms and achieves an impressive accuracy of 95%, outperforming traditional machine learning models.

Context-Aware Sentiment Analysis on Product Reviews Using Hybrid Model

Sentiment analysis and sarcasm detection as become an important area in natural language processing (NLP) due to growth of e-commerce and social media platforms. Customers give feedback through reviews which helps to understand the contextual meaning and sentiment present in the text.The system integrates DistilBERT and CNN-LSTM hybrid model where CNN (Convolutional Neural Network) used to extract the local features, and LSTM (Long Short-Term Memory) learns sequential and long-term dependencies in the text, and DistilBERT is used to capture contextual embeddings in the text. The proposed system processes the reviews through preprocessing techniques including text cleaning, negation handling, and tokenization. Experimental results shows that the hybrid model achieves high accuracy compared to standalone and architecture architectures. The proposed model is applied in e-commerce platforms, recommendation systems and customer feedback analysis for better decision making.

A Recommender System for Developing Marketing Strategies for Online Products Based on Sentiment Analysis of User Reviews Using Deep Learning Algorithms

This study aimed to develop a recommender system for formulating marketing strategies for online products based on sentiment analysis of user reviews and to compare the performance of deep learning algorithms in predicting product sales. This applied, descriptive-analytical study was based on textual data obtained from users of an online store. User reviews of products were employed as the primary input data, and the sentiments expressed in the reviews were classified into positive, negative, and neutral categories. Three deep learning algorithms—Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM)—were implemented for sentiment analysis and sales prediction. The model incorporated product- and review-related features, including user sentiment, price, brand, quality, delivery, packaging, support, warranty, and other product characteristics. Model performance was comparatively evaluated using accuracy, precision, F1-score, recall, confusion matrices, and receiver operating characteristic (ROC) curves. Comparative results demonstrated that CNN achieved the strongest overall performance, followed by LSTM and RNN. Accuracy values were 0.966, 0.960, and 0.953 for CNN, LSTM, and RNN, respectively. Corresponding precision values were 0.956, 0.954, and 0.944; F1-scores were 0.931, 0.917, and 0.904; and recall values were 0.964, 0.957, and 0.949. The confusion-matrix and ROC analyses further supported the relative superiority of CNN in classification and prediction. Feature-importance analysis based on the CNN model indicated that other users’ comments had the greatest contribution to classification, followed by user sentiment, delivery, brand, and packaging. Sentiment analysis of user reviews using deep learning algorithms, particularly CNN, can provide an effective analytical basis for predicting product sales and developing recommender systems that support product-specific marketing strategy formulation in online retail environments.

Enhancing recommendation systems with weighted aspect based opinion mining utilizing advanced deep learning for adaptive personalization of user preferences

Graph Attention-Based Engineering Framework for Aspect-Level Sentiment-Driven Recommendation Using BiLSTM–CRF Hybrid Model

A novel recommendation algorithm that combines Aspect-Based Sentiment Analysis (ABSA) and a Graph Attention Network (GAT) to improve the recommendation quality and allow for improved interpretability because of the aspect-level sentiment representation.