Latent Dirichlet allocation

Python (programming language)

Naive Bayes classifier

Knowledge management

Operating system

Support vector machine

World Wide Web

Open innovation

Artificial intelligence

Topic model

Data science

Computer science

Social media

Sentiment analysis

Classifier (UML)

Technovation

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 the sample of n = 586.348 tweets into three groups expressing the following three sentiments: positive, negative, and neutral. Then, we used a mathematical topic modeling algorithm known as Latent Dirichlet allocation to analyze the tweet databases. Finally, Python was used to develop textual analysis techniques under the theoretical framework of Computer-Aided Text Analysis and Natural Language Processing. The results revealed that, in the tweets dataset, there were eight topics. Of these topics, two contained tweets expressing negative sentiments (Culture and Business Models/Management), three topics contained tweets expressing positive sentiments (Communities, Creative projects and Ideas), and three topics contained tweets expressing neutral sentiments (Entrepreneurship, Teams and Technology). These topics are discussed in the context of limitations, risks, and characteristics of open innovation according to the UGC on Twitter. The paper concludes with the formulation of 20 limits of open innovation and 27 research questions for further research on open innovation, as well as a discussion of theoretical and practical implications of the study.

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