In recent years, the sentiment analysis on data messages from social networks has attracted high attention of researchers. However, most of their works have been focused on classifying user messages to positive (or like) and negative (or dislike) on social issues or discussion topics. In addition, they usually only worked with English messages from a single data source, or a domain. In this paper, we proposed a crossed-domain sentiment analysis system for the discovery of current careers from social networks. The proposed system can capture sentiment of careerrelated messages from two famous social networks, including Twitter and Facebook. The experimental results clearly pointed out that the most favorite careers which enjoy the highest positive sentiment and the least favorite careers that have the highest negative sentiment. The performance results of the proposed system are promising for crossed-domain sentiment analysis, with the precision of over 85% and the recall of over 90%.
The recommendation system integrated in movie streaming provides relevant information to viewers predicted by viewers’ past behaviors. There are basically two methods, Content-Based Filtering and Collaborative Filtering. In this article, our focus is on the second method which is based on memory, namely Neighborhood-based Collaborative Filtering (NBCF), to make movie recommendations to users given users’ information. Simultaneously, we have built an online movie website and integrated the movie recommendation system based on NBCF to assist users in movie selection. In the process of building the website, apart from building diagram of movie recommendation system’s functions, class diagram of movie recommendation function, sequence diagram of movie recommendation function, we also build a user-recommended movie model based on the Movies Lens[9] dataset for a fairly high accuracy, which is 99%.
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