Private entrepreneurs and government organizations widely adopt Facebook fan pages as an online social platform to communicate with the public. Posting on the platform to attract people’s comments and shares is an effective way to increase public engagement. Moreover, the comment functions allow users who have read the posts to express their thoughts. Hence, it also enables us to understand the users’ emotional feelings regarding that post by analyzing the comments. The goal of this study is to investigate the public image of organizations by exploring the content on fan pages. In order to efficiently analyze the enormous amount of public opinion data generated from social media, we propose a Bi-directional Long Short-Term Memory (BiLSTM) that can model detailed sentiment information hidden in those words. It first forecasts the sentiment information in terms of Valence and Arousal (VA) values of the smallest unit in a text, and later fuses this into a deep learning model to further analyze the sentiment of the whole text. Experiments show that our model can achieve state-of-the-art performance in terms of predicting the VA values of words. Additionally, combining VA with a BiLSTM model results in a boost of the performance for social media text sentiment analysis. Our method can assist governments or other organizations to improve their effectiveness in social media operations through the understanding of public opinions on related issues.