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Crowdfunding in the realm of the Social Web has received substantialattention, with prior research examining various aspects of campaigns,including project objectives, durations, and influential project categories forsuccessful fundraising. These factors are crucial for entrepreneurs seekingdonor support. However, the terrain of charity crowdfunding within the SocialWeb remains relatively unexplored, lacking comprehension of the motivationsdriving donations that often lack concrete reciprocation. Distinct fromconventional crowdfunding that offers tangible returns, charity crowdfundingrelies on intangible rewards like tax advantages, recognition posts, oradvisory roles. Such details are often embedded within campaign narratives,yet, the analysis of textual content in charity crowdfunding is limited. Thisstudy introduces an inventive text analytics framework, utilizing LatentDirichlet Allocation (LDA) to extract latent themes from textual descriptionsof charity campaigns. The study has explored four different themes, two each incampaign and incentive descriptions. Campaign description themes are focused onchild and elderly health mainly the ones who are diagnosed with terminaldiseases. 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 ForestClassifier to predict success of the campaign using both thematic and numericalparameters. The study distinguishes thematic categories, particularly medicalneed-based charity and general causes, based on project and incentivedescriptions. In conclusion, this research bridges the gap by showcasing topicmodelling utility in uncharted charity crowdfunding domains.

A Latent Dirichlet Allocation (LDA) Semantic Text Analytics Approach to Explore Topical Features in Charity Crowdfunding Campaigns