Crowdfunding Campaigns Success Prediction Through Natural Language Processing

Master's Thesis from the year 2021 in the subject Business economics - Company formation, Business Plans, grade: 1,0, Hamburg University of Technology (Institute of Entrepreneurship), language: English, abstract: This thesis examines whether a data mining approach, such as natural language processing, can help the founders of crowdfunding campaigns be more successful. In a data mining framework 493,324 campaigns of the two popular crowdfunding platforms Kickstarter and Indiegogo were analyzed by natural language processing using different artificial neural networks to obtain the information needed by the founders. For frequently occurring categories, a reliable classification of the category was possible. For rare ones it was less precise. It was also shown that the more a founder concentrates on a specific category when setting up a campaign, the more likely it was that a campaign would be successful. A prediction of campaign success was also possible but was influenced by the nature of the data set. It was demonstrated that this approach could generate important information that could lead to a competitive advantage of the founders for most of the campaigns in the dataset. Crowdfunding is an emerging industry which has gained considerable attention in recent years. Competition among campaigns and founders will therefore become increasingly intense. This means, that founders must gain a competitive advantage over the competitors to be successful. Data mining approaches which also include natural language processing could be suitable to assist the founders with valuable information when setting up campaigns and thus enable them to gain a competitive advantage. Especially the right categorization on a crowdfunding platform and prediction of success are important information to support the founders.

This book constitutes the refereed proceedings of the 8th International Conference on Information Management and Big Data, SIMBig 2021, held as a virtual event in December 2021. The 25 revised full papers and 2 revised short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in topical sections on data mining and applications; deep learning and applications; data-driven software engineering; health, NLP, and social media; image processing, machine learning, and semantic web.

This book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics.

Responding to the increasingly blurred boundaries between humans and technology, this innovative Handbook reveals the intricate patterns of interaction between individuals, machines, and organizations. Using cutting-edge data and analysis, expert contributors provide new insight into the rapidly growing digitalization of society.

This book constitutes the conference proceedings of the 16th International Symposium on Intelligent Data Analysis, which was held in October 2017 in London, UK. The 28 full papers presented in this book were carefully reviewed and selected from 66 submissions. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. IDA solicits papers on all aspects of intelligent data analysis, including papers on intelligent support for modelling and analyzing data from complex, dynamical systems.

In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms that extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. This book will discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts, and it shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, health care, and business intelligence. The book further covers the existing evaluation metrics for NLP and social media applications and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks), the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC), or the Conference and Labs of the Evaluation Forum (CLEF). In this third edition of the book, the authors added information about recent progress in NLP for social media applications, including more about the modern techniques provided by deep neural networks (DNNs) for modeling language and analyzing social media data.