Discover how three independent AI models analyze the same movie review, explore the technology behind the platform, and learn how The Screening Room brings machine learning and modern software engineering together.
The Statistician evaluates a review by measuring how strongly individual words and phrases indicate positive or negative sentiment. It provides a fast, reliable baseline and is highly interpretable.
The Contextualist reads every word in relation to every other word, allowing it to understand context, nuanced language, and complex sentence structures.
The Sequentialist processes each review in order, learning how meaning develops across a sentence. It captures long-term dependencies and contextual flow within the text.
| Model | Accuracy | Precision | Recall | F1 | Inference |
|---|
Metrics are reported using the final evaluation performed on the held-out IMDb test dataset.
The models were trained and evaluated using the IMDb Large Movie Review Dataset, a widely used benchmark for binary sentiment classification introduced by Andrew L. Maas and colleagues.
Dataset: IMDb Large Movie Review Dataset
Authors: Andrew L. Maas et al.
Institution: Stanford AI Lab
Official Source: https://ai.stanford.edu/~amaas/data/sentiment/
FastAPI powers the machine learning inference service because it provides high-performance APIs while integrating naturally with Python-based ML libraries.
Express separates application logic from machine learning inference by handling validation, routing, persistence, and API orchestration.
PostgreSQL stores prediction history and model metadata using a relational schema to demonstrate production-style persistence.
The goal is not only to predict sentiment but also to compare how fundamentally different NLP approaches interpret the same review.
| Challenge | Solution | Result |
|---|---|---|
| Different preprocessing requirements | Model-specific preprocessing pipeline | Consistent and reliable predictions |
| Three incompatible prediction outputs | Unified response schema | Common API contract across all models |
| Large BERT model | Singleton loading during application startup | Reduced inference overhead after initialization |
| Different confidence formats | Standardized prediction response | Consistent frontend rendering |
Support additional languages beyond English.
Analyze multiple reviews simultaneously.
Compare more state-of-the-art NLP models.
Responsive mobile-first interface.
Expand interpretation techniques for deeper insight into model behavior.