Topic modeling and sentiment analysis of social media data to drive experiential redesign

The elicitation of customer pain points is a crucial early step in the design or redesign of successful products and services. Online, user-generated data contains rich, real-time information about customer experience, requirements, and preferences. However, it is a nontrivial task to retrieve useful information from these sources because of the sheer amount of data, often unstructured. In this work, we build on previous efforts that used natural language processing techniques to extract meaning from online data and facilitate experiential redesign and extend them by integrating a sentiment analysis. As a use case, we explore the airline industry. A considerable portion of potential passengers opt out of traveling by airplane due to aviophobia, a fear of flying. This causes a market loss to the industry and inconvenience for those who experience aviophobia. The potential contributors to aviophobia are complex and diverse, involving physical, psychological and emotional reactions to the air travel experience. A methodology that is capable of accommodating the complexity and diversity of the commercial airline industry user-generated data is necessary to effectively mine customer pain points. To address the demand, we propose a novel methodology in this study. Using passenger commentary data posted on Reddit, the method implements topic modeling to extract common themes from the commentaries and employs sentiment analysis to elicit and interpret the salient information contained in the extracted themes. This paper ends by providing specific recommendations that are germane to the use case as well as suggesting future research directions.