Mining Social Media for Open Innovation in Transportation Systems
This work addresses the need for companies to monitor public reception and diffusion of products using social media, but it is incremental as it applies existing text analysis techniques to a specific case.
The authors tackled the problem of leveraging social media data for open innovation in transportation by analyzing tweets about Uber to measure the impact of a controversial event on tweet volume and user perception, finding a large increase in mentions but no change in Uber's image.
This work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data. The framework is able to extract users comments from Online Social Networks (OSN), to process and analyze text through information extraction and sentiment analysis techniques to obtain relevant information about product reception on the market. A use case was developed using the mobile application Uber, which is today one of the fastest growing technology companies in the world. We measured how a controversial, highly diffused event influences the volume of tweets about Uber and the perception of its users. While there is no change in the image of Uber, a large increase in the number of tweets mentioning the company is observed, which meant a free and important diffusion of its product.