Dynamic Large Scale Data on Twitter Using Sentiment Analysis and Topic Modeling

Abstract

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 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. We use Naïve Bayes Sentiment Analysis based on time-series, specifically on daily basis and topic modeling based on Latent Dirichlet Allocation (LDA) to evaluate the sentiment of the topic as well as the model of the topics discussed. This research may help both companies and individuals to map the public opinion towards certain topic by analyzing the sentiment of the text and create a topic model. Therefore, a real-time information for determining the consumer opinion become a crucial part. Twitter can serve the purpose as one source of realtime information from user-generated content. We pick Uber as the case study, viewed as one of the most favored transportation methods in most part of the world. Data collection period is from 10th February 2017 until 28th February 2017 with 1.048.576 tweets collected.