Level 1 Case StudyDeep Learning / Mar 2025

News Headline Classification

I built an LSTM-based deep learning model that classifies news headlines with 93.28% accuracy on a dataset of 400K+ headlines.

93.28% accuracy25% training efficiency improvement

TL;DR

I built an LSTM-based deep learning model that classifies news headlines with 93.28% accuracy on a dataset of 400K+ headlines.
Best published result: 93.28% accuracy

Automatically categorize news headlines into topics for content organization News platforms, content aggregators. Manual classification is time-consuming; automated systems improve discoverability.

personal / research with defined system ownership
Problem framing: Automatically categorize news headlines into topics for content organization
Architecture: LSTM neural network with embedding layers
Implementation: spaCy for preprocessing, PyTorch for training
Evaluation: 93.28% accuracy; 25% training efficiency improvement
Before: Manual classification is time-consuming; automated systems improve discoverability
Personally designed: Used LSTM over simpler models like logistic regression because Captures sequential dependencies in text better than bag-of-words approaches; Optimized preprocessing pipeline because 25% efficiency improvement in training
Others owned: External datasets, APIs, academic baselines, or hackathon constraints shaped the work; the project page calls out what the source data verifies.

FlowHeadline Classification system flow

LSTM neural network with embedding layers; spaCy for preprocessing, PyTorch for training; Predicted category labels with confidence scores

input 01Input
Raw news headlines (text)
process 02Backend
input ->
LSTM neural network with embedding layers
storage 03Data / storage
backend ->
spaCy for preprocessing, PyTorch for training
external 04External APIs
backend ->
None
output 05Output
storage ->external ->
Predicted category labels with confidence scores

Routes

Rejected: Transformer-based models, but LSTM was more interpretable. Chosen path: Used LSTM over simpler models like logistic regression.
Rejected: Raw text without optimization. Chosen path: Optimized preprocessing pipeline.
LSTM training is slower than simpler models but provides better accuracy
Model requires GPU for practical training time

93.28% accuracy - Correct classification rate on test set - Correct classification rate on test set - resume.json
25% training efficiency improvement - Speed of preprocessing and training pipeline - Speed of preprocessing and training pipeline - resume.json

Why this architecture boundary exists: LSTM neural network with embedding layers
How I evaluated Correct classification rate on test set
The hardest tradeoff: LSTM training is slower than simpler models but provides better accuracy
What I would change next: Explore attention mechanisms for explainability