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Use Case Summary

Use-Case-Summary

An OTT content distributor preparing to launch 8 new titles across various streaming platforms wanted to understand audience sentiment around trailers, cast, and pre-release buzz.

They partnered with OTT Scrape to perform real-time sentiment analysis across IMDb and Rotten Tomatoes—scraping reviews, early ratings, critic summaries, and fan forums. The goal: predict how each upcoming title would perform before investing in aggressive marketing or promotional spend.

The outcome? A 30% improvement in pre-launch decision accuracy, more focused campaigns, and better regional segmentation—all powered by data.

Business Challenge

Business-Challenge

The Problem:

Most content performance metrics come post-launch. By then, it’s too late to rework positioning or withdraw promotional spend.

The client asked:

  • Can we predict early sentiment shifts for unreleased content?
  • Can we detect cast-based or genre-based polarity in fan discussions?
  • Can we compare trailer reactions across multiple OTT platforms?

Goals:

  • Monitor sentiment from IMDb user reviews, forums & “anticipated watchlists”
  • Scrape Rotten Tomatoes critic scores and early fan buzz
  • Extract and classify reviews/comments by emotion (positive, neutral, negative)
  • Use NLP-based tagging to surface themes, cast mentions, and expectations

Solution: Multi-Source Sentiment Scraping by OTT Scrape

Solution-Multi-Source-Sentiment-Scraping-by-OTT-Scrape

OTT Scrape deployed a dual-channel web scraping and NLP-based text analysis pipeline targeting:

  • IMDb: forums, user reviews, trailer comments, “Most Anticipated” lists
  • Rotten Tomatoes: critic blurbs, fan ratings, upcoming release watchlists
  • Other sources: YouTube trailer comment sections (optional layer)

SEO Keywords Used:

SEO-Keywords-Used
  • OTT sentiment analysis tools
  • IMDb review scraping
  • Rotten Tomatoes data extraction
  • Predict OTT show success
  • Content performance prediction

Sample Scraped & Processed Data

json
CopyEdit
[
  {
    "title": "Edge of Reality",
    "platform": "Prime Video",
    "release_date": "2025-07-12",
    "imdb_pre_release_rating": 7.8,
    "rotten_tomatoes_critic_score": 84,
    "sentiment_summary": {
      "positive": 72,
      "neutral": 19,
      "negative": 9
    },
    "common_tags": ["psychological thriller", "strong female lead", "mind-bending"]
  },
  {
    "title": "Street Vibe",
    "platform": "Netflix",
    "release_date": "2025-07-20",
    "imdb_pre_release_rating": 6.1,
    "rotten_tomatoes_critic_score": 58,
    "sentiment_summary": {
      "positive": 42,
      "neutral": 28,
      "negative": 30
    },
    "common_tags": ["generic plot", "dance drama", "low expectations"]
  }
]

Key Metrics Delivered

Metric Description
IMDb Pre-Release Score Ratings given by early viewers or test audiences
RT Critic Score % of critics giving a positive review
Comment Sentiment Classification NLP-based emotion tagging on scraped comments
Common Themes Extracted keywords/phrases from discussions
Viewer Anticipation Index Composite score of mentions, tone, trailer view count

Analysis from OTT Scrape

Analysis-from-OTT-Scrape

1. Content Type & Sentiment Correlation

  • Thrillers & Biopics had the highest pre-launch positivity
  • Dance dramas & sequels showed high sentiment polarity (divided reactions)

2. Cast-Based Bias Detection

  • Titles featuring rising stars or critically acclaimed actors had >20% boost in positive sentiment
  • Franchise fatigue was evident for sequels with recurring casts

3. Regional Split Indicators

  • “Edge of Reality” had higher sentiment in urban U.S. regions
  • “Street Vibe” was better received in Latin American discussions but had negative feedback in North America

Impact of Using OTT Scrape

KPI Before (Last Launch Cycle) After (This Cycle w/ Sentiment Data)
Campaign Budget Wastage 26% 12%
High-Risk Titles Pulled Pre-Launch 0 2
Regional Campaign Re-Allocation No Yes (5 countries re-targeted)
Accuracy of Demand Prediction 62% 89%

The sentiment signals allowed the client to adjust promotions, drop risky titles, and double down on likely breakout hits.

Dashboard Delivered by OTT Scrape

Dashboards-Delivered-by-OTT-Scrape
  • Real-time sentiment graphs
  • Top keywords & actor mentions
  • Region-wise fan engagement heatmap
  • Positive/negative spikes over 7-day trailer windows
  • Critic rating trendline (pre-release vs launch)

How OTT Scrape Solved Technical Challenges

Challenge Solution by OTT Scrape
Rate-limited IMDb forums & reviews Rotating proxies + headless scraping
Text noise in comments NLP-based cleaning + emotion modeling
Rotten Tomatoes critic extraction DOM parsing + structured data API parsing
Duplicate data from fan sites Deduplication logic in pre-processing pipeline

Final Recommendations Provided

  • Greenlight “Edge of Reality” for global push + press interviews
  • Reduce spend on “Street Vibe” in English-speaking markets
  • Advance pre-release promotions for thrillers with female leads
  • Avoid July 20–25 window due to heavy negative buzz on other OTT titles

Strategic Value for Studios & Distributors

Strategic-Value-for-Studios-&-Distributors
  • Predict title success before a dollar is spent on launch
  • Track genre-specific sentiment shifts over time
  • Fine-tune regional rollouts based on viewer expectation
  • Leverage top fan comments in pre-launch marketing
  • Pivot or pause content before committing to full release

Final Thoughts

In a world where OTT content launches daily, guessing what works isn’t enough. With OTT Scrape’s sentiment analysis from IMDb and Rotten Tomatoes, you can make decisions powered by real audience voices — even before your content premieres.

Studios that use sentiment intelligence can avoid bad launches, maximize hype, and plan content with confidence.