A crossed-domain sentiment analysis system for the discovery of current careers from social networks

Abstract

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 career-related 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%.