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Entrepreneurship has flourished in recent years; however, since education on how to raise funds has received little attention from scholars, obtaining funds remains a difficult task. The development of crowdfunding has provided new opportunities to entrepreneurs, thus solving the funding, marketing, and distribution problems they previously faced. The main purpose of this study is to organize crowdfunding literature and to develop a crowdfunding curriculum grounded on output-based education. Students are asked to develop a product and a crowdfunding plan within the span of one semester. This study explains the teaching content separately from the crowdfunding plan competition, course elements, and timetable. [ABSTRACT FROM AUTHOR]

• Comments are classified into antecedent comments and posterior comments. • FastText is better than other algorithms for sentiment analysis for crowdfunding reviews. • FNN achieves better prediction performance than other approaches. This study focuses on the predictive power of online reviews on fundraising outcomes. Based on when the review is posted, we divide these online reviews into antecedent comments, made at the prelaunch and financing stages, and posterior comments, after the fundraising stage. Then, we conduct a sentiment analysis of these online reviews with a variety of algorithms to determine which one is best at sentiment extraction, finding that the deep learning algorithm is more effective than the shallow learning algorithms. Using fastText, we find that, as financing progresses, sentiment in the comments gradually turns negative, and sentiment strength is lower for successfully funded projects than for failed projects. The reason for this pessimistic turn is the topic discussed at various stages. FNN performs better than other approaches in the prediction of successful fundraising outcomes, with the addition of the antecedent online comments' sentiment factor. Other prediction algorithms also show some degree of improvement with the addition of sentiment extracted from antecedent online reviews, which confirms the predictive power of antecedent comments. This study enhances the identification of the economic value of antecedent online reviews with the addition of sentiment analysis and shows a path for further developing internet finance. [ABSTRACT FROM AUTHOR]

As the information communication and investment of crowdfunding campaigns are usually accomplished online, online crowdfunding theoretically breaks the limitation of space. However, investors’ behaviors still show significant home bias; furthermore, from a dynamic view, the changes in home bias during the funding duration reflect the investment preference. This paper studies the dynamics of home bias in crowdfunding investment as well as the distance diffusion of home bias in the crowdfunding market through data mining and econometric models. The distance between investors and founders gradually increases from 3605 km to 4229 km as the funding progresses, and home bias shows different diffusion patterns between categories. For most of the campaigns, the distance between backers and founders of the successful campaigns is always greater than that of the failed ones; thus, the distance diffusion impacts the pledge results. However, the study also finds that for some categories, home bias does not play a positive role in investment or that it even shows negative impacts, among which food and technology are two extreme categories. The study provides a theoretical basis from the perspective of user behavior to analyze online investment and to improve the promotion of crowdfunding campaigns. [ABSTRACT FROM AUTHOR]

A crowdfunding narrative often has a title, abstract, detailed description, reward statements, and the creators' biographies, which carry elements of subjectivity and objectivity in their wordings. The subjectivity and objectivity facets of an entrepreneurial narrative influence the signal's observability, thus affecting the fundraising outcomes. This study contributes to the understanding of the subjectivity versus objectivity debate in economic exchange in online crowdfunding, by examining how and if the crowdfunding outcomes can be better achieved through the deliberate positioning of the subjectivity versus objectivity claims in the various text sections and through carefully determining the prioritization of the subjective and objective contents. This study employs text mining and Bayesian inference to identify and differentiate the subjective and objective attributes in an entrepreneurial narrative. Taking 328,974 campaigns from Kickstarter , from April 2009 to April 2019, we observe a statistical difference between the text sections in a narrative. Some level of objective appeal should reside in the abstract and the reward statement. To court investors, the title, detailed textual description, and biography should be phrased subjectively. For the detailed textual description, the objective contents should be positioned at the start of the narrative, followed by the subjective statements, to enhance the success of online fundraising. Further, the overall prediction accuracy of funding support in the presence of subjective versus objective factors improves by 1.76%, from 76.37%, as shown through machine learning prediction analytics. • Measure the subjectivity vs. objectivity for large entrepreneurial corpus. • Evaluate the impact of subjectivity vs. objectivity among the text sections. • Objective appeal should reside in abstract and reward statement. • Title, detail textual description, and biography can be worded subjectively. [ABSTRACT FROM AUTHOR]