Project Overview
Sentiment analysis is one of the most impactful NLP applications in industry, powering customer feedback systems, social media monitoring, and brand reputation management. In this project, you will work with the Sentiment140 dataset containing 1.6 million tweets labeled as positive or negative. You will build two models: a traditional LSTM neural network and a state-of-the-art BERT transformer, comparing their performance on text classification. Target accuracy: over 85% with LSTM and over 90% with BERT.
Preprocessing
Clean text, remove noise, handle emojis and special characters
Tokenization
Convert text to sequences, build vocabulary, pad sequences
LSTM Network
Build recurrent architecture with embeddings and dropout
BERT Fine-tuning
Adapt pre-trained transformers for classification
Learning Objectives
Technical Skills
- Implement comprehensive text cleaning pipelines
- Use Keras Tokenizer and TensorFlow text processing
- Build LSTM models with embedding layers
- Fine-tune BERT using Hugging Face Transformers
- Evaluate NLP models with precision, recall, F1-score
NLP Concepts
- Understand word embeddings and vector representations
- Learn attention mechanisms in transformers
- Compare RNN vs transformer architectures
- Handle class imbalance in text classification
- Interpret model predictions with attention weights
Business Scenario
SocialPulse Analytics
You have been hired as a Senior NLP Engineer at SocialPulse Analytics, a social media intelligence company that helps brands understand customer sentiment in real-time. The company processes millions of social media posts daily to provide insights about brand perception, product feedback, and emerging trends. Your task is to build the core sentiment classification engine.
"Our clients need real-time sentiment analysis that is both fast and accurate. We need two models: a lightweight LSTM for high-throughput processing and a BERT model for high-stakes analysis where accuracy is critical. Can you build both and help us understand the trade-offs?"
Technical Challenges to Solve
- How to handle Twitter-specific tokens (@mentions, #hashtags)?
- Should emojis be removed or converted to text?
- How to deal with slang, abbreviations, and misspellings?
- What is the optimal text length for truncation?
- How many LSTM layers are optimal?
- Bidirectional LSTM vs unidirectional?
- Which BERT variant to use (base, distilled)?
- How to prevent overfitting on noisy social data?
- LSTM: faster inference vs lower accuracy
- BERT: higher accuracy vs slower inference
- Memory and compute requirements comparison
- When to use which model in production?
- How to handle sarcasm and irony?
- Which metrics matter most (accuracy, F1, AUC)?
- How to analyze model errors?
- Confidence calibration for predictions
The Dataset
You will work with the Sentiment140 dataset, one of the most popular datasets for sentiment analysis. Download from Kaggle or use alternative datasets based on your preference:
Primary Dataset: Sentiment140
The Sentiment140 dataset contains 1.6 million tweets automatically labeled using emoticons as noisy labels. Tweets with positive emoticons (like :)) are labeled positive (4), and tweets with negative emoticons (like :() are labeled negative (0).
Sentiment140 Schema
| Column | Type | Description |
|---|---|---|
target | Integer | Sentiment label: 0 = negative, 4 = positive |
ids | Integer | Unique tweet ID |
date | String | Tweet timestamp (e.g., "Mon Apr 06 22:19:45 PDT 2009") |
flag | String | Query flag (NO_QUERY for all) |
user | String | Twitter username |
text | String | Tweet content (max 140 characters) |
Sample Data
| Sentiment | Text |
|---|---|
| Negative | "@user I hate when that happens. My whole day is ruined now" |
| Positive | "Just had the best coffee ever! Starting my day right" |
| Negative | "Stuck in traffic again. This commute is killing me" |
| Positive | "Can't wait for the weekend! Beach trip with friends" |
Project Requirements
Your project must include all of the following components. This is a comprehensive NLP project covering both classical deep learning (LSTM) and modern transformer approaches (BERT).
Data Loading and Exploration
Load and understand the dataset:
- Load Sentiment140 data (or chosen alternative)
- Explore class distribution (positive vs negative)
- Analyze text length distribution
- Sample and display example texts from each class
- Create train/validation/test splits (80/10/10)
Text Preprocessing Pipeline
Build a comprehensive text cleaning pipeline:
- Convert to lowercase
- Remove URLs, mentions (@user), and hashtags (or convert hashtags to words)
- Handle emojis (remove or convert to text using emoji library)
- Remove special characters and numbers
- Remove stopwords (optional - experiment with/without)
- Apply lemmatization or stemming
- Remove extra whitespace
Tokenization and Embeddings
Convert text to numerical representations:
- Use Keras Tokenizer to build vocabulary
- Convert texts to sequences of token IDs
- Pad sequences to uniform length (choose appropriate max length)
- Experiment with pre-trained embeddings (GloVe or Word2Vec)
- Compare trainable vs frozen embeddings
LSTM Model Development
Build and train an LSTM sentiment classifier:
- Design architecture: Embedding > LSTM > Dense layers
- Experiment with bidirectional LSTM
- Add dropout for regularization
- Compile with binary crossentropy and Adam optimizer
- Use callbacks: EarlyStopping, ModelCheckpoint
- Train and monitor validation metrics
Target Performance: Over 85% accuracy on test set
BERT Fine-tuning
Fine-tune a pre-trained BERT model:
- Load pre-trained BERT (bert-base-uncased or DistilBERT)
- Use Hugging Face Transformers and Datasets libraries
- Tokenize text with BERT tokenizer
- Add classification head on top of BERT
- Fine-tune with appropriate learning rate (2e-5 to 5e-5)
- Monitor training with Trainer or custom loop
Target Performance: Over 90% accuracy on test set
Model Evaluation and Comparison
Comprehensive evaluation of both models:
- Calculate accuracy, precision, recall, F1-score
- Generate confusion matrices for both models
- Create ROC curves and calculate AUC
- Analyze misclassified examples
- Compare inference speed (predictions per second)
- Document trade-offs between LSTM and BERT
Text Preprocessing Pipeline
Text preprocessing is crucial for NLP models. Social media text requires special handling for mentions, hashtags, URLs, and informal language.
Preprocessing Decisions
- Keep emojis: Convert to text ("happy_face")
- Keep negations: "not", "no", "never" are crucial
- Keep intensifiers: "very", "really", "extremely"
- Convert hashtags: #happy to "happy"
- Lemmatize: Reduces vocabulary size
- Remove all stopwords: Loses "not happy" context
- Aggressive stemming: Can distort meaning
- Remove all emojis: Valuable sentiment signals
- Remove repeated chars: "sooo good" shows emphasis
- Over-cleaning: Loses important signals
LSTM Model Architecture
Long Short-Term Memory (LSTM) networks are effective for sequence data like text. They can capture long-range dependencies that simple RNNs miss.
Recommended Architecture
| Layer | Type | Parameters | Output Shape |
|---|---|---|---|
| Input | InputLayer | max_length=100 | (100,) |
| Embedding | Embedding | vocab_size, embed_dim=128 | (100, 128) |
| SpatialDropout | SpatialDropout1D | rate=0.2 | (100, 128) |
| LSTM1 | Bidirectional LSTM | 64 units, return_sequences | (100, 128) |
| LSTM2 | Bidirectional LSTM | 32 units | (64,) |
| Dense1 | Dense | 64 units, ReLU | (64,) |
| Dropout | Dropout | rate=0.5 | (64,) |
| Output | Dense | 1 unit, Sigmoid | (1,) |
Fine-tuning BERT
BERT (Bidirectional Encoder Representations from Transformers) achieves state-of-the-art results on many NLP tasks. Fine-tuning involves adapting the pre-trained model to your specific task.
LSTM vs BERT Comparison
| Aspect | LSTM | BERT |
|---|---|---|
| Accuracy | ~85-88% | ~90-93% |
| Training Time | Minutes (CPU/GPU) | Hours (GPU required) |
| Inference Speed | Fast (~1000 samples/sec) | Slower (~100 samples/sec) |
| Model Size | ~10-50 MB | ~400-800 MB |
| Pre-training Required | Optional (embeddings) | Yes (uses pre-trained weights) |
| Best Use Case | High-throughput, resource-limited | High-accuracy, batch processing |
Submission Requirements
Create a public GitHub repository with the exact name shown below:
Required Repository Name
sentiment-analysis-nlp
Required Project Structure
sentiment-analysis-nlp/
├── notebooks/
│ ├── 01_data_exploration.ipynb # EDA and preprocessing
│ ├── 02_lstm_model.ipynb # LSTM development
│ └── 03_bert_finetuning.ipynb # BERT fine-tuning
├── src/
│ ├── preprocessing.py # Text cleaning functions
│ ├── lstm_model.py # LSTM architecture
│ └── bert_model.py # BERT fine-tuning code
├── models/
│ ├── lstm_sentiment.h5 # Saved LSTM model
│ └── bert_sentiment/ # Saved BERT model folder
├── reports/
│ ├── confusion_matrix_lstm.png # LSTM confusion matrix
│ ├── confusion_matrix_bert.png # BERT confusion matrix
│ ├── training_curves.png # Training history plots
│ └── model_comparison.png # LSTM vs BERT comparison
├── requirements.txt # Python dependencies
└── README.md # Project documentationREADME.md Required Sections
1. Project Header
- Project title and description
- Your full name and submission date
- Final accuracy for both models
2. Text Preprocessing
- Cleaning steps implemented
- Before/after examples
- Vocabulary statistics
3. LSTM Model
- Architecture diagram/summary
- Training configuration
- Final metrics achieved
4. BERT Fine-tuning
- Model variant used
- Training hyperparameters
- Final metrics achieved
5. Model Comparison
- Accuracy, F1, inference speed
- Trade-offs discussion
- Recommendation for production
6. How to Run
- Installation instructions
- How to train models
- How to make predictions
Enter your GitHub username - we will verify your repository automatically
Grading Rubric
Your project will be graded on the following criteria. Total: 600 points.
| Criteria | Points | Description |
|---|---|---|
| Data Exploration | 50 | EDA, class distribution, text analysis, proper splits |
| Text Preprocessing | 75 | Comprehensive cleaning, tokenization, embedding preparation |
| LSTM Model | 125 | Architecture design, training, achieving over 85% accuracy |
| BERT Fine-tuning | 150 | Proper fine-tuning, achieving over 90% accuracy |
| Model Comparison | 100 | Metrics comparison, trade-off analysis, recommendations |
| Documentation | 75 | README quality, code comments, notebook organization |
| Bonus: Error Analysis | 25 | Deep analysis of misclassified examples, edge cases |
| Total | 600 |
Grading Levels
Excellent
BERT over 92%, exceptional analysis
Good
Both models meet targets, good docs
Satisfactory
Meets minimum requirements
Needs Work
Missing components or low accuracy
Ready to Submit?
Make sure both models are trained and your comparison analysis is complete.
Submit Your ProjectPre-Submission Checklist
Use this checklist to verify you have completed all requirements before submitting.