This US-based technology company has transformed how the entertainment industry understands viewer behavior. Using machine learning and predictive analytics, they forecast how audiences may react to entertainment content, like upcoming trailers, shows, or movies. Instead of outdated survey-based research, their AI tool predicts audience engagement and helps content creators and distributors effectively reach target viewers.
The client needed expert data labeling services to improve their machine learning model’s performance. This project required resources with in-depth knowledge of cinema, storytelling, and genre to ensure accurate metadata tagging. Our assignment involved assigning precise, context-specific keywords (covering genre, themes, emotions, characters, and audience appeal) to each storyline, providing critical inputs for the client's AI models to predict target audience behavior.
We had to label video and text files, including:
Our team's deliverables included:
| High-Volume Content Metadata Tagging | Multilingual Text & Video Labeling |
|---|---|
| Accurately tagging over 2,500+ entertainment assets per month (movies, series, trailers) with context-specific keywords. | Assigning accurate tags to content across diverse cultural contexts and languages, including Spanish and German. |
The project demanded a rare combination of artistic understanding (the annotator must be able to interpret storytelling, genres, mood, themes, and characters, not just surface facts) and structured, consistent tagging/labeling to ensure contextually accurate data annotation at scale, leading to certain challenges on our part.
The labeling process couldn't be generic. So, it required annotators who understood genre-specific nuances (horror, sci-fi, romance, documentaries, international cinema, etc.) and had an extensive understanding of the entertainment industry for precise metadata annotation.
We faced a direct conflict between the necessity for deep analysis and strict delivery timelines. The client required us to maintain contextual accuracy across unique storylines while meeting the daily quota of 80+ content analyses and document tagging. This large volume demanded scalable data labeling workflows that could sustain this throughput without sacrificing quality.
Every single TV show, movie, or trailer presented a unique narrative that defied simple, template-based categorization. Accurate data tagging often required annotators to develop a fresh contextual perspective and employ robust web research service capabilities. This was necessary to decode plot intricacies, cross-reference cultural details, and validate specific thematic elements before assigning keywords.
The project required linguistic expertise beyond English, specifically demanding accurate content analysis, label determination, and video and text annotation in languages like Spanish and German. Because the interpretation of narratives and the appropriateness of keywords are deeply cultural, we needed data annotators with native-level language expertise to ensure the assigned keywords were linguistically sound and culturally relevant for those target markets.
While automated content annotation would have been great for processing over 2,500+ shows and movies per month efficiently, the nuances and cultural context embedded in the media meant there was a high potential for contextual error and poor audience targeting. Also, choosing completely manual video and text labeling would have been too slow and inefficient for a project of this scale.
So, we implemented a human-in-the-loop (HITL) data labeling approach.
We began by deploying 25 dedicated resources combining entertainment expertise with data entry precision:
We established a precise, multi-layered methodology:
Each content piece, be it trailer, synopsis, or show description, was analyzed and broken down into multiple narrative layers to fully grasp the essence before any keywords were assigned. This included:
Where themes were nuanced or culturally rooted, annotators performed web research to cross-check interpretations and refine keyword choices for precise audience targeting.
To ensure that the annotated dataset reflected not just the content, but also its appeal to specific audiences, we used a semantic mapping approach to assign keywords that served the following two critical functions:
To ensure high labeling consistency across thousands of titles, we developed a structured keyword ontology framework. This system organized key terms into a hierarchical structure of genres, moods, and themes, acting as both a dictionary and a roadmap to classify content.
This framework eliminated any unnecessary invention of subjective terms by annotators and hence ensured consistency at scale. For example, terms like “Detective” and “Investigation” were placed under the broader parent category “Crime/Thriller.” This standardization enabled accurate and scalable labeling.
To validate data annotation and ensure accurate training data, we implemented a robust multi-tier text labeling and video labeling workflow using a human-in-the-loop approach:
Protecting the client's confidential content—often involving pre-release entertainment assets—was non-negotiable. We guaranteed end-to-end security throughout the data labeling lifecycle by implementing specific, stringent protocols that exceeded standard security measures:
Strict adherence to ISO 27001-certified practices covering secure data storage, reliable transfer mechanisms, and access management.
Team members working on the project signed comprehensive Non-Disclosure Agreements (NDAs) to enforce strict confidentiality.
Client’s content databases’ access was secured using robust controls, like multi-factor authentication (MFA) and biometric access controls.
Maintained strictly segregated network environments using VPN-secured connections and real-time data access monitoring to prevent unauthorized exposure.
We strengthened the client’s operational efficiency and significantly improved the accuracy of their AI model. Additionally, our multilingual data annotation support enabled the client to expand confidently into Spanish and German markets, ensuring the platform stayed relevant across languages and regions.
Achieved through accurately labeled, context-rich data, resulting in more reliable audience behavior forecasts and predictive algorithms.
Elevated final accuracy by +13–14% from the client's internal benchmark (85%) through human-in-the-loop precision.
Increased content analysis and labeling volume from ~60 assets per day to ~100 assets per day, supporting rapid data scaling.
Standardized protocols and multi-tier validation minimized inconsistencies, significantly improving overall dataset quality and model reliability.
Reduced delivery time from 3–4 days to 24–48 hours, streamlining the client's development pipeline.
Streamlined labeling workflows and scalable team deployment helped the client advance their product development roadmap ahead of schedule.
Enabled entry into new language regions by delivering multilingual data annotation, aligning with local linguistic and cultural nuances.
Does your machine learning model struggle because standard labeling doesn’t capture enough context or detail? Or are you struggling to scale annotation while maintaining contextual accuracy?
Our specialized text, video, and image annotation services can be customized to your use case (e.g., storyline tagging, character sentiment labeling, or annotating scene locations) and the nuances of your industry. We deliver high-quality training data for AI, enriched with both surface-level attributes and deeper semantic meaning.
Request a free sample to evaluate our training data quality or reach out to know more about our data annotation services and customized labeling capabilities.
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