Understanding Public Opinion towards New Sharing Economy Business Model Using Content Analysis

Hospitality, as one of the sectors in the tourism industry, continues to show positive trend every year based on its revenues. In order to understand public opinion, the legacy method such as interview, questionnaire, and other time-consuming method is still widely used in this sector. However, this method is less efficient compared to data analytics methodology using available data from online source, such as social media Therefore, we need to conduct a research to see a better method in order to understand the public opinion in hospitality sector. There are many methodologies to support content analysis based on unstructured data to understand public opinion. In this research, we use content analysis methodology which consists of Sentiment Analysis, Topic Modeling, and Text Network Analysis to process 483413 obtained tweets from January 7
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until March 28
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, 2017. The twitter data is gained using Python and R language. The whole process of the research consists of data collection, data preprocessing, and interpreting the results. We use Rstudio, Jupyter Notebook and Gephi softwares during the process. As a case study in the hospitality sector, we use Airbnb as one of the sharing economy business models that allows other people to share their space and begins to be widely used by travelers around the world. Our objective is to understand public opinion towards Airbnb as online-based sharing economy business model which becomes a disruptive innovation for tourism industry. As the result, we are able to apply the combination of content analysis methods to give a good understanding towards the public opinion. Text Network Analysis gives us the ability to summarise large-scale conversation in very fast and real-time fashion. It provides the knowledge by associate most frequent words used in social conversations. Sentiment Analysis shows the feelings and emotion from people about certain topics. Topic Modelling is able to capture popular topics, thus gives comprehensive understanding on how people react towards a new business model.