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As privacy concerns increase and regulation against tracking-based advertisingtightens, contextual advertising—which targets ads based on webpage content ratherthan personal data—offers a compelling alternative. The shift towards this alternativeform of ad targeting is gaining momentum thanks to advancements in artificialintelligence (AI), which significantly improve the ability to interpret and categorizeonline content. This thesis explores how AI can interpret online contexts and leveragethem for targeted, privacy-conscious marketing.A key contribution is the development of methods for extracting opinions from textand structuring them into “opinion units”, leveraging the power and versatility oflarge language models. Opinion units consist of concise, context-rich excerpts thatcapture individual opinions, paired with sentiment metadata. The proposed methodsdemonstrate high accuracy in opinion extraction and show promise for downstreamapplications. For instance, in opinion search and topic modeling of customer reviews,the compactness and distinctness of opinion units enhance retrieval precision andproduces more coherent and interpretable groupings of opinions. This enables theidentification of specific aspects driving customer satisfaction, providing insights forproduct development and targeted marketing.Marketing experiments conducted in this thesis reveal how media contexts influenceadvertising perceptions. The findings demonstrate that engaging content and thecredibility of website sources create a spillover effect, enhancing the effectiveness ofassociated ads. Regarding brand safety—ensuring ads do not appear in brand-damaging contexts—the results suggest that proximity to negative news articles aloneis not directly harmful. However, marketers face increased risks when the advertisedmessage is associated with the negative context. To mitigate these risks, AI tools canbe used to detect and avoid potentially unsafe online environments.Finally, the thesis offers guidance on AI-driven ad targeting by outlining the trade-offsbetween contextual and personalized strategies, as well as manual versus automatedmethods. The discussion considers key factors such as marketing objectives, dataavailability, and ethical considerations alongside regulatory requirements. Thefindings serve as a foundation for making well-informed, strategic choices in thefuture of advertising targeting.