An Approach to Integrating Sentiment Analysis into Recommender Systems.

Area of Science:

• Artificial Intelligence
• Data Science
• Human-Computer Interaction

Background:

• Recommender systems enhance user satisfaction and product consumption across various domains.
• Sentiment analysis of social data offers insights into user attitudes, opinions, and emotions.
• Integrating sentiment analysis can improve the reliability of recommendations by complementing explicit user ratings.

Purpose of the Study:

• To develop and evaluate a novel recommendation approach.
• To integrate sentiment analysis techniques into collaborative filtering methods.
• To enhance recommender system performance through sentiment-informed personalization.

Main Methods:

• An adaptive architecture for the recommender system was designed.
• Advanced feature extraction techniques were employed.
• Deep learning models incorporating sentiment analysis were utilized.
• Collaborative filtering methods were enhanced with sentiment data.

Main Results:

• Empirical studies were conducted on two popular datasets.
• Sentiment-based deep learning models demonstrated significant performance improvements.
• The integration of sentiment analysis with collaborative filtering led to higher recommendation reliability.
• The proposed approach showed substantial gains in recommender system effectiveness.

Conclusions:

• Sentiment analysis is a valuable tool for improving recommender systems.
• Deep learning models combined with sentiment data offer a powerful approach to personalization.
• The integration strategy significantly boosts the performance of collaborative filtering recommenders.