A Latent Dirichlet Allocation (LDA) Semantic Text Analytics Approach to
  Explore Topical Features in Charity Crowdfunding Campaigns
Prathamesh Muzumdar George Kurian Ganga Prasad Basyal
Crowdfunding in the realm of the Social Web has received substantial
attention, with prior research examining various aspects of campaigns,
including project objectives, durations, and influential project categories for
successful fundraising. These factors are crucial for entrepreneurs seeking
donor support. However, the terrain of charity crowdfunding within the Social
Web remains relatively unexplored, lacking comprehension of the motivations
driving donations that often lack concrete reciprocation. Distinct from
conventional crowdfunding that offers tangible returns, charity crowdfunding
relies on intangible rewards like tax advantages, recognition posts, or
advisory roles. Such details are often embedded within campaign narratives,
yet, the analysis of textual content in charity crowdfunding is limited. This
study introduces an inventive text analytics framework, utilizing Latent
Dirichlet Allocation (LDA) to extract latent themes from textual descriptions
of charity campaigns. The study has explored four different themes, two each in
campaign and incentive descriptions. Campaign description themes are focused on
child and elderly health mainly the ones who are diagnosed with terminal
diseases. Incentive description themes are based on tax benefits, certificates,
and appreciation posts. These themes, combined with numerical parameters,
predict campaign success. The study was successful in using Random Forest
Classifier to predict success of the campaign using both thematic and numerical
parameters. The study distinguishes thematic categories, particularly medical
need-based charity and general causes, based on project and incentive
descriptions. In conclusion, this research bridges the gap by showcasing topic
modelling utility in uncharted charity crowdfunding domains.