A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 4; pp. 2124 - 2135
• Main Authors: Rosa, Renata Lopes, Schwartz, Gisele Maria, Ruggiero, Wilson Vicente, Rodriguez, Demostenes Zegarra
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-04-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Data mining; Deep learning; Distance learning; Disturbances; Electronic devices; Informatics; Knowledge based systems; Knowledge bases (artificial intelligence); knowledge personalization and customization; Machine learning; Measurement; Mental depression; Messages; Monitoring; Monitoring systems; Neural networks; Ontologies; Ontology; recommendation system; Recommender systems; Recurrent neural networks; Sentences; Sentiment analysis; Social networks; Stress; User satisfaction
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2018.2867174

Online social networks provide relevant information on users' opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a convolutional neural network and a bidirectional long short-term memory - recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.