Project Scope
Literature Survey
Movie trailers are one of the most important marketing tools in the film industry. With the growth of platforms such as YouTube, audiences now respond to trailers publicly through views, likes, comments and shares, long before a film is released.
Predicting that response is still difficult. Studios rely on manual observation, focus groups, post-release feedback or basic engagement statistics. These methods are slow, expensive and subjective, and they cannot explain why a trailer performs the way it does.
Previous studies have addressed parts of this problem separately: box-office prediction, social media sentiment, movie review analysis and general engagement metrics. Comment analysis is made harder by slang, emoji, sarcasm and mixed languages common in trailer comments.
Key observations
- Engagement data is plentiful but fragmented across platforms.
- Views and likes show how much attention a trailer gets, not why.
- Video, audio, sentiment and popularity are usually analysed in isolation.
- Most systems stop at a prediction and offer no improvement guidance.
Research Gap
| Limitation in existing work | How this research addresses it |
|---|---|
| Trailer analysis often depends on manual interpretation | ✓Automated AI-based analysis pipeline |
| Systems analyse only text, only popularity, or only video/audio | ✓Multimodal analysis using video, audio, comments and metrics |
| Basic sentiment tools struggle with informal trailer comments | ✓Transformer-based (BERT) comment sentiment analysis |
| Popularity metrics are used only descriptively | ✓Machine-learning-based popularity prediction |
| Video/audio analysis rarely provides scene-level emotional intensity | ✓Per-scene emotional intensity scoring |
| Prediction systems lack actionable recommendations | ✓Explainable recommendation engine (MTIRF) |
Research Problem & Solution
Research Problem
How can film studios and marketing teams reliably predict audience reaction to a movie trailer, and understand what to improve, when engagement data is large, fragmented and difficult to interpret manually?
Proposed Solution
An AI-powered platform that analyses trailer visuals, audio, viewer comments and popularity metrics, fuses them into a unified trailer profile, predicts audience reaction, and generates ranked, explainable recommendations through a web dashboard.
System Demonstration
Demo video will be embedded here
Demo video will be embedded here
Research Objectives
Main objectiveTo design and develop an AI-powered movie trailer analysis system that predicts audience reactions and engagement by combining video and audio features, viewer comment sentiment, popularity metrics and an explainable recommendation framework.
SUB-OBJECTIVE 01
Video & Audio Analysis
Extract scene-level visual, emotional and acoustic features from trailers.
- Object and motion detection
- Facial emotion recognition
- Audio energy analysis
- Emotional intensity per scene
SUB-OBJECTIVE 02
Popularity Metrics Analysis
Predict audience reaction from YouTube engagement metrics.
- Views, likes and comments
- Like ratio and engagement rate
- View growth indicators
- Reaction-level prediction
SUB-OBJECTIVE 03
Comment Sentiment Analysis
Classify viewer comments and extract audience opinion topics.
- Comment preprocessing
- Language detection
- BERT sentiment classification
- Topic extraction
SUB-OBJECTIVE 04
Insight & Recommendation Engine
Fuse all outputs and generate ranked improvement recommendations.
- Audience reaction prediction
- Feature importance analysis
- Benchmark gap analysis
- Recommendation cards
Methodology
The system follows a modular architecture. Each component processes one type of trailer data and passes structured output to a fusion layer, which drives the final prediction and recommendations.
Figure 1: High-level system architecture
1. Data AcquisitionVideos, comments and metrics from YouTube
2. ProcessingPreprocess video, audio, text and metrics
3. AI AnalysisVision, NLP, audio and ML models
4. Feature FusionCombine component outputs
5. PredictionReaction level and recommendations
6. DashboardCharts, labels and recommendation cards
Technologies Used
PythonCore language for AI and analytics
YouTube Data APITrailer metadata, comments, metrics
OpenCVFrame extraction and motion analysis
YOLOv8Object detection in frames
DeepFaceFacial emotion recognition
CLIPZero-shot scene classification
LibrosaAudio feature extraction
FFmpegAudio and video processing
BERT (Hugging Face)Transformer sentiment classification
TensorFlowDeep learning models
PyTorchDeep learning models
scikit-learnML training and evaluation
PandasData processing
NumPyNumerical computing and features
FastAPIBackend REST APIs
FlaskSentiment service API
ReactWeb dashboard
ViteFrontend build tool
FirebaseAuthentication and data storage
DockerContainerised deployment