Abstract
This paper introduces a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns. Our study emphasizes the need to understand crowdfunding from an investor’s perspective. Linguistic styles in crowdfunding campaigns that aim to trigger excitement or are aimed at inclusiveness are better predictors of campaign success than firm-level determinants. At the contrary, higher uncertainty perceptions about the state of product development may substantially reduce evaluations of new products and reduce purchasing intentions among potential funders. Our findings emphasize that positive psychological language is salient in environments where objective information is scarce and where investment preferences are taste based. Employing enthusiastic language or showing the product in action may capture an individual’s attention. Using all technology and design-related crowdfunding campaigns launched on Kickstarter, our study underscores the need to align potential consumers’ expectations with the visualization and presentation of the crowdfunding campaign.
Highlights
This paper introduces a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns
Both Logistic Regression (LR) and a Linear Support Vector Classifier (LinearSVC) exhibit the best classification, which suggests that the classification of campaign success might be determined by partially linearly scaled features in our data
This study employs a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video object–related metadata in 20,188 crowdfunding campaigns
Summary
This paper introduces a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns. While Parhankangas and Renko (2017) find that commercial entrepreneurs need to primarily focus on product, or firm and entrepreneur-related signals in their textual descriptions, other work shows that in low attention states visual cues work best, while textual information become only relevant if a high attention has been triggered previously (Allison et al 2017). Much progress has been made toward artificial intelligence, using machine learning systems that are trained to replicate the decisions of human experts (LeCun et al 2015) These expert systems (Hayes-Roth et al 1983) tackled challenging domains in terms of human intellect, such as image recognition (He et al 2016), language translation (Wu et al 2016), medical image classification (Esteva et al 2017), mastering board games Go, Shogi, or Chess (Silver et al 2016, 2017, 2018), playing computer games (Mnih et al 2015), and achieved or exceeded human-level performance (LeCun et al 2015). In the economics domain, machine learning techniques and methods on causal inferences entered the econometric toolbox (Varian 2014; Athey and Imbens 2017; Kleinberg et al 2017; Mullainathan and Spiess 2017; Belloni et al 2014)
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