Intelligent Recommendations of Startup Projects in Smart Cities and Smart Health Using Social Media Mining

Area of Science:

• Computer Science
• Information Systems
• Urban Planning

Background:

• The increasing need for innovative solutions in smart cities and smart health sectors.
• The potential of social media data, specifically tweets, for identifying emerging trends and opportunities.
• The challenge of connecting aspiring entrepreneurs with suitable projects and skill requirements.

Purpose of the Study:

• To develop and implement a recommendation model for smart city and smart health startup projects.
• To enhance entrepreneurship and leadership by providing targeted project recommendations to citizens.
• To contribute to national economic advancement by reducing unemployment and improving national income.

Main Methods:

• Utilizing tweet mining through the Word2Vec machine learning algorithm.
• Employing an ontology-based recommendation technique for project and skill matching.
• Designing and implementing a system to validate the proposed approach.

Main Results:

• The developed approach successfully processes and analyzes tweets related to smart city and smart health startups.
• The system identifies relevant startup projects and links them to necessary user skills and competencies.
• Achieved performance metrics: 95% precision, 66% recall, and 79% F-measure.

Conclusions:

• The proposed tweet mining and ontology-based recommendation approach is effective for identifying smart city and smart health startup opportunities.
• The system demonstrates encouraging performance, validating its utility in fostering entrepreneurship.
• The model has the potential to support national economic development through job creation and innovation.