Intelligent Recommender System Model through Sentimental Analysis
DOI:
https://doi.org/10.69996/fmep.2025028Keywords:
Sentiment Analysis, Recommender System, Deep Learning, Convolutional Neural Network (CNN), User Emotion DetectionAbstract
The exponential growth of online content has intensified the need for intelligent recommender systems that can deliver personalized suggestions to users. This paper presents a novel approach that integrates sentiment analysis with a deep learning-based recommendation framework to enhance the relevance and emotional alignment of recommendations. By analyzing user-generated content such as reviews and comments, the system extracts sentiment scores to understand user preferences and emotional tones. Convolutional Neural Networks (CNN) is employed to train on the sentiment-labeled dataset, enabling precise classification of opinions. The model dynamically adapts to user sentiment, filtering and prioritizing items based on positive or negative feedback. A comprehensive user interface supports dataset upload, training, prediction, and visualization of recommendations. Experimental results demonstrate the effectiveness of the proposed system in improving user satisfaction and engagement by delivering recommendations that resonate with individual emotional contexts. This approach bridges the gap between traditional recommendation logic and human-like understanding, offering a more intuitive and user-centric experience.
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