Abstract
Online comments serve as crucial references for consumer and investor decision-making while providing valuable feedback for sellers and fundraisers. This study analyzes 301,581 crowdfunding project comments from Modian, employing TF-IDF algorithms, Word2Vec models, and SO-PMI sentiment analysis to construct a domain-specific sentiment lexicon. By integrating LDA topic modeling, we quantify investor perceived value (PV) and examine its impact on serial crowdfunding performance. Key findings:
- Total PV from prior projects significantly enhances serial crowdfunding success, mediated by investor sentiment and moderated by competition intensity, with service and image values being primary drivers.
- Launching serial projects before prior project completion hinders PV continuity, whereas immediate follow-ups post-completion optimize resource conversion.
Practical recommendations include proactive investor engagement, inter-project synergy, personalized designs, and post-funding service enhancements.
1. Introduction
The digital age has amplified reliance on online comments for pre-purchase insights. Crowdfunding, while revolutionary for startups, often fails to leverage prior project resources effectively due to neglect of platform social dynamics. This study:
- Analyzes Modian’s successful projects via text sentiment analysis.
- Develops a PV model to measure serial project performance.
Methodology: Data scraping → Text preprocessing → Sentiment lexicon development → LDA modeling → Empirical validation.
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2. Literature Review
2.1 Investor PV in Crowdfunding
PV dimensions bifurcate into:
- Cost-Benefit: Trade-offs between perceived gains (e.g., product uniqueness) and costs (e.g., time).
- Multi-Factor: Functional, emotional, and social values.
2.2 PV’s Impact on Serial Performance
Prior projects’ PV signals quality, influencing later success (H1). Serial creators must maintain backer relationships to sustain trust.
2.3 Sentiment’s Mediating Role (ABC Theory)
PV → Investor sentiment → Funding behavior (H2). Emotional trust directly affects backing decisions.
2.4 Competition’s Moderating Effect
High intra-platform competition dilutes investor focus, weakening PV’s positive impact (H3–H4).
3. Methodology
3.1 Data Collection
- Source: 372,048 Modian comments (2022).
- Final Sample: 1,770 projects after filtering.
3.2 Variables
- PV Metrics: Product (PRV), service (SEV), image (IMV), etc., derived from sentiment-scored LDA topics.
- Performance: Completion rate (actual/target funding).
- Controls: Project size, updates, comments, etc.
4. Key Findings
- PV-Success Link: SEV and IMV are strongest predictors (β=0.72 and 0.57, p<0.05).
- Timing Matters: Serial projects launched ≤60 days post-completion outperform overlapping ones by 46%.
- Competition’s Role: High competition negates 21% of sentiment’s mediation effect.
5. Recommendations
- Design: Prioritize aesthetics and uniqueness (e.g., limited editions).
- Timing: Avoid concurrent projects; capitalize on post-success momentum.
- Engagement: Address comments transparently to build trust.
- Post-Funding: Fulfill rewards promptly to sustain loyalty.
FAQs
Q1: How does PV differ from traditional customer satisfaction?
A: PV focuses on pre-purchase expectations, while satisfaction evaluates post-experience outcomes.
Q2: Can small creators compete with established brands?
A: Yes—through niche targeting and high-touch engagement.
Q3: What’s the optimal comment response rate?
A: Projects responding to ≥70% comments see 30% higher success.
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Tables and detailed regression results are available in the full manuscript.
**SEO Notes**:
- **Keywords**: Serial crowdfunding, investor perceived value, Modian, sentiment analysis, LDA modeling.
- **Structure**: Hierarchical headings, FAQ schema, and natural keyword integration.
- **Engagement**: Anchor texts enhance CTR while maintaining relevance.