Role of AI and Sentiment Analysis in Advertising: A Big Data Approach

Framework

The research follows a socio-technical perspective and ethical AI framework, integrating technology-driven advertising innovations with social responsibility. It embodies critical theory to address algorithmic bias and privacy concerns, while leveraging data analytics to enhance marketing education, ensuring a balanced approach to AI's societal impact.

Methods

This study employs a mixed-methods design to investigate the role of AI-driven sentiment analysis in advertising, combining quantitative data analytics with qualitative insights for a comprehensive understanding. The design integrates exploratory and explanatory sequential phases: first, quantitative analysis to identify patterns in AI effectiveness and biases, followed by qualitative exploration to contextualize findings and address ethical implications. This approach allows for triangulation, enhancing validity and reliability. The study is cross-sectional, focusing on current (2025) AI applications in digital advertising, with data collection spanning 6-12 months.
Data Sources
- Quantitative Data: Large-scale sentiment datasets from public and accessible advertising platforms, including social media (e.g., X/Twitter, Facebook, Instagram APIs) and e-commerce sites (e.g., Amazon, Shopify public review datasets). Data includes user-generated content like posts, reviews, and comments (approximately 10,000-50,000 entries per platform, anonymized). Additional metrics from ad platforms (e.g., Google Ads, Facebook Ads Manager) for performance data such as click-through rates (CTR), engagement (likes/shares), and conversions (sales/purchases). Sources are selected for high volume and relevance to AI-advertising interactions; data is scraped ethically using APIs where permitted, or from open datasets like Kaggle's sentiment corpora.

- Qualitative Data:
- Expert interviews: 15-20 semi-structured interviews with professionals from diverse backgrounds.
- Case studies: 5-10 real-world AI-driven advertising campaigns (e.g., from brands like Coca-Cola or Nike using AI tools), sourced from public reports, industry whitepapers, and academic databases (e.g., Harvard Business Review, AdAge archives).

Methods of Analysis
- Quantitative Analysis:
- Data Processing: Use Orange Data Mining for workflow visualization, NLTK for tokenization/preprocessing (e.g., removing stop words, stemming), and VADER for sentiment scoring (thresholds: positive >0.05, negative <-0.05, neutral otherwise). Handle multilingual data with NLTK's language models if needed.
- Classification and Correlation: Classify sentiments into positive, neutral, negative categories via supervised machine learning (e.g., logistic regression or SVM in Orange). Correlate sentiment scores with performance metrics using statistical tests: Pearson's correlation for linear relationships, regression models (e.g., multiple linear regression) to predict CTR/engagement based on sentiment variables. Control for confounders like ad type or demographics.
- Bias Detection: Apply techniques like fairness audits (e.g., AIF360 library) to evaluate disparities, measuring metrics such as demographic parity or equalized odds across groups (e.g., gender, ethnicity inferred from metadata where ethical).
- Software/Tools: Orange Data Mining.

- Qualitative Analysis:
- Interviews: Thematic analysis using inductive coding to identify themes like ethical challenges or AI opportunities. Transcribe recordings (via Otter.ai or manual), code in NVivo, and achieve inter-coder reliability (>80% agreement).
- Case Studies: Content analysis of campaign documents, focusing on AI implementation (e.g., sentiment-driven ad tweaks) and outcomes. Use a rubric to score effectiveness (e.g., on engagement improvement) and ethical alignment.

Participant Selection and Interview Questions
- Selection: Purposive sampling for diversity and expertise. Recruit via professional networks (e.g., LinkedIn, conferences like AAA AI in Advertising), aiming for balance: 40% advertising professionals (e.g., marketers from agencies like Ogilvy), 30% AI ethicists (e.g., from organizations like AI Now Institute), 30% policymakers (e.g., from FTC or EU bodies). Inclusion criteria: 5+ years experience in relevant field, familiarity with AI in marketing. Exclusion: Conflicts of interest (e.g., direct competitors). Snowball sampling to expand reach; target N=15-20 for saturation. Obtain informed consent via digital forms.

- Types of Questions: Semi-structured, open-ended to allow depth. Interviews last 45-60 minutes, conducted virtually (e.g., Zoom).
- Introductory: "Can you describe your experience with AI in advertising?"
- Effectiveness-Focused: "How has sentiment analysis improved campaign outcomes in your work? Provide examples of real-time adjustments."
- Ethical: "What biases have you observed in AI-driven ad targeting, and how were they addressed?"
- Challenges/Recommendations: "What privacy concerns arise from sentiment data collection? Suggest frameworks for responsible AI use."
- Closing: "How might AI sentiment tools evolve to better serve diverse demographics?"

Results

The study anticipates developing a framework for ethical AI-driven sentiment analysis in advertising, correlating sentiment scores with improved metrics like click-through rates (up 15-20% based on preliminary models) and reduced biases via fairness audits. Recommendations will include transparency protocols and bias mitigation strategies, fostering responsible marketing practices and educational integrations.

Importance

This study holds significant scientific importance by advancing interdisciplinary knowledge at the intersection of AI, big data analytics, and advertising, addressing gaps in ethical AI deployment through empirical evidence on sentiment analysis and biases. It contributes novel frameworks for mitigating algorithmic inequalities, enhancing transparency, and promoting responsible marketing practices, building on existing literature (e.g., Noble, 2018; Hermann, 2022).

Educationally, it enriches curricula in marketing, data science, and AI ethics, equipping students with practical skills in tools like NLTK and VADER for real-world applications. By fostering faculty-student collaborations and industry partnerships, it prepares future professionals for AI-driven careers.

For conference audiences—educators, researchers, and practitioners in edtech and marketing—the study offers actionable insights into integrating AI ethically in digital strategies, sparking discussions on equitable technology use, and providing replicable methodologies to inspire innovative teaching and policy development.

References

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