Abstract
The paper presents a recommendation model for developing new smart city and smart health projects. The objective is to provide recommendations to citizens about smart city and smart health startups to improve entrepreneurship and leadership. These recommendations may lead to the country's advancement and the improvement of national income and reduce unemployment. This work focuses on designing and implementing an approach for processing and analyzing tweets inclosing data related to smart city and smart health startups and providing recommended projects as well as their required skills and competencies. This approach is based on tweets mining through a machine learning method, the Word2Vec algorithm, combined with a recommendation technique conducted via an ontology-based method. This approach allows discovering the relevant startup projects in the context of smart cities and makes links to the needed skills and competencies of users. A system was implemented to validate this approach. The attained performance metrics related to precision, recall, and F-measure are, respectively, 95%, 66%, and 79%, showing that the results are very encouraging.
Highlights
We focus on the recommendation of startup projects in the context of smart cities for Saudi youth, based on what users have posted in tweets. ese tweets can be seen as raw resources and reach information exchanged between individuals. us, this work focuses on designing and implementing an approach for processing and analyzing tweets inclosing data related to smart city startups. e objective is to provide recommendations to users about smart city and smart health startups to improve entrepreneurship and leadership following their skills and competencies
The recommendations are based on an OWL ontology created for this purpose
In the first algorithm (Algorithm 1), the preprocessing step includes the following: translation, cleaning of tweets, tokenization, and lemmatization. e Google application programming interface (API) was used for the translation of the tweets. e remaining tasks, such as cleaning, as well as tokenization and lemmatization, were carried out with the support of the Natural Language Toolkit (NLTK) libraries [68]
Summary
E objective is to provide recommendations to users about smart city and smart health startups to improve entrepreneurship and leadership following their skills and competencies. We focus on the recommendation of startup projects in the context of smart cities for Saudi youth, based on what users have posted in tweets. For this purpose, first, extracted tweets are preprocessed (cleaning, stop word removal, tokenization, and lemmatization). The fourth section will discuss the research findings, based on the experimentations and tests and evaluations of the implemented system. e final section, Conclusion, will emphasize some future works to enhance the attained results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
