Exploring the boundaries of open innovation: Evidence from social media mining

Technological development of the last several decades has driven open innovation towards organizational, business, social, and economic change. Open innovation has emerged as the main driver of change in a business sector that needs to be flexible and resilient, rapidly adapting to change through innovation. In this context, the present study aimed to explore the limits of open innovation by extracting evidence from user-generated content (UGC) on Twitter using social media mining. To this end, in terms of the methodology, we first applied machine learning Sentiment Analysis algorithm texted using Support Vector Classifier, Multinomial Naïve Bayes, Logistic Regression, and Random Forest Classifier to divide…

• MD
  Ministerio de Ciencia, Innovación y Universidades

  Award: RTI2018
• ER
  European Regional Development Fund

  Award: RTI2018-096295-B-C22