The Study of Social Media, Deep Learning and Sentiment Analysis for the Methods of Personalized Recommendation

• Main Authors: HENDRY, 洪進國
• Other Authors: CHEN, RUNG-CHING
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/35xd43

博士 === 朝陽科技大學 === 資訊管理系 === 106 === Social media and the development of web 2.0 encourage the user to participate more interactively in social networks. In the social network, the relationships may be identified by the user posts and interactions. Using this data, the system can make recommendations tailored to specific users. However, when the user is on the social network for the first time, the recommendation system cannot make recommendations, since the user has no history. In this paper, we design an ontology combined with social networks. We develop the ontology based on data from users and their friends. Using the user interest and community influences, we propose a system to solve the cold start problem in recommendation systems. The system calculates the similarity among users. Then, user preferences and a rule generating algorithm create the dynamic inference rule. The ontology is updated each time the content of the personal ontology is updated. The newest ontology will be retained to increase the accuracy the next time the recommendation system is executed. Deep learning is one of the methodologies which are applied to neural networks and learning that have been applied in many fields and have achieved many breakthrough successes for many applications. User comments are important for recommender systems because they include various types of emotional information that may influence the correctness or precision of the recommendation. How to improve the accuracy of user rating from obtained feasible recommendations is important. In this paper, we propose a deep learning model to process user comments and to generate a feasible user rating for the recommendation. First, the system uses sentiment analysis to create a feature vector as the input nodes. Next, the system implemented a noise reduction in the dataset to improve the classification of user ratings. Finally, generate training model by a deep belief network and sentiment analysis (DBNSA). The experimental results indicated the system has better accuracy than traditional methods.