Abstract
In the growing reliance on social media for brand campaigns, this study aims to identify efficient and reliable methodologies for evaluating their success. Using a quantitative approach, we investigated two key metrics: sentiment analysis (via VADER) and Brand Search Volume (BSV) via Google Trends. We employed an OLS regression model to examine how sentiment scores and negative-to-positive comment ratios influence BSV. The findings reveal strong positive correlations between sentiment scores, total comment counts, and BSV changes in the subsequent month (28.3% increase per sentiment score unit) and year (6.57% increase). Higher negative-to-positive comment ratios were significantly associated with BSV declines, with negative sentiments showing persistent short- and long-term impacts. The study highlights the need to integrate emotional response metrics (e.g., sentiment analysis) with behavioural indicators (e.g., BSV) to assess campaigns’ effects on consumer attitudes and behaviour. It emphasises that managers and policymakers must ensure campaigns secure initial positive reception and maintain long-term relevance.
| Original language | English |
|---|---|
| Article number | 7 |
| Pages (from-to) | 88-98 |
| Number of pages | 11 |
| Journal | Annals of Emerging Technologies in Computing (AETiC) |
| Volume | 9 |
| Issue number | 5 |
| Publication status | Published - 25 Oct 2025 |
Keywords
- Brand search volume
- OLS regression
- Sentiment analysis
- Social media evaluation
- Video branding campaigns