Digital flows now exert a larger impact, the world is now more connected than ever, the amount of cross-border bandwidth that used has grown 45 times larger since 2005. With the massive amount of data spreading in the net, including social media. Speed is one most essential factor in business. companies can take advantage of social media as a source to be analyze and extract the customer’s opinion, and therefore the company can have quick response towards the condition. The main purpose of this research is content analysis, to obtain the goal, we need to extract the information as well as summarize the topic inside it. However, in order to analyze the content quickly, there are varies choice of tools with its specific output that creates challenges in the process. The author use text network to map the connection between word, Naïve Bayes Sentiment Analysis, and topic modelling based on Latent Dirichlet Allocation (LDA) to evaluate the sentiment of the topic as well as the model of the topics discussed. This research is intended to help both companies and individuals to map the public opinion towards certain topic by visualizing network of the word, analyzing the sentiment of the text, and create a topic model. Therefore, a real time information for determining the consumer opinion become crucial part. Twitter, Facebook, Instagram as most used platform in the world can serve the purpose as a source of real time information from user generated content. The author picks automotive industry, with General Motors and Volkswagen brand as the case study, viewed as one of the most known automotive brand, and topped off the revenue of automotive brand sales.