System design
Defined the model approach and data handling flow for multiclass emotion recognition.
AI · NLP · TEXT CLASSIFICATION
6-Class Emotion Classification
Multiclass emotion detection using transformer and recurrent modeling for short-form text.
A text-classification project designed to detect six emotional states from short written expressions using a hybrid NLP pipeline built around DistilBERT and BiGRU.
TEXT → EMOTION SIGNAL6-CLASS NLP MODEL
happysadangryafraidlovesurprisefeelneedfamilyhurtwishhopetodaymindthoughtkindafraidcare
JOY
SADNESS
ANGER
FEAR
LOVE
SURPRISE
01 / THE PROBLEM
Emotion recognition from text is nuanced and context-dependent.
Short text often carries layered emotional signals. Words can be ambiguous, sarcastic, or emotionally mixed, which makes emotion classification more challenging than simple topic detection.
This project explores how a transformer-based model can capture the semantic context behind text and classify it into six emotional categories with clearer intent.
02 / MY ROLE
AI / ML engineer building the emotion-classification pipeline.
Data preprocessing
Prepared and normalized text to support consistent transformer and sequence modeling inputs.
Model development
Implemented the DistilBERT + BiGRU architecture and trained the classification model.
Evaluation
Assessed model behavior using training diagnostics and classification metrics.
03 / DATA & PREPROCESSING
Structured text data into a clean multiclass training flow.
6 classesLabel count
Text sequencesModel input
Transformer + recurrent encoderRepresentation
Multiclass emotion classificationObjective
RAW TEXT
“I feel broken and overwhelmed, but also hopeful for tomorrow.”
→
PREPROCESSING
clean text, normalize casing, tokenize, prepare model-ready sequences
→
MODEL INPUT
DistilBERT contextual embeddings + BiGRU sequence encoding
04 / MODEL ARCHITECTURE
A hybrid transformer-recurrent classifier.
01TEXT INPUT
02PREPROCESSING
03DISTILBERT
04BiGRU
05CLASSIFICATION HEAD
066 EMOTION CLASSES
JOYSADNESSANGERFEARLOVESURPRISE
05 / TRAINING & EVALUATION
Monitored learning behavior and classification quality.
Training vs Validation Loss
Evaluation Focus
- Accuracy
- Precision
- Recall
- F1-score
06 / RESULTS
The model translates context into emotion labels.
PROJECT OUTCOME
6-class emotion classification model for short-form, textual emotion understanding.
The work demonstrates a practical NLP approach for contextual emotion detection, pairing Transformer-based embeddings with a recurrent sequence model to improve classification over raw text-only baselines.
KEY ADVANTAGE
Captures nuanced affective context in text and classifies it into six distinct emotional states.
APPLICATION
Useful for sentiment-aware systems, conversational analysis, and human-centered text understanding.
07 / TECHNOLOGY STACK
Text, modeling, and evaluation built in Python.
PythonPyTorchTransformersDistilBERTBiGRUscikit-learnPandasNumPyNLTKTensorBoard
Explore the Emotion Classifier Repository
View the model implementation, training workflow, DistilBERT + BiGRU pipeline, and evaluation setup.