Goal : Build a basic working model first, then iteratively improve it.
- Data Collection
- Preprocessing: Clean tweets (remove URLs, hashtags, emojis, stopwords).
- Baseline Model: Use Bag-of-Words/TF-IDF + Logistic Regression (simple but effective). Train/test split (e.g., 80/20). Evaluate accuracy/F1-score.
- Study Concepts Along the Way Focus on these key areas (prioritize what’s needed for your next step):
A. Text Preprocessing Techniques: Lowercasing, tokenization, handling slang/emojis, stemming/lemmatization.
Interview Question: "How would you handle hashtags or emojis in tweets?"
B. Feature Extraction Bag-of-Words (BoW) vs. TF-IDF: Learn pros/cons (BoW ignores word order; TF-IDF weights rare words).
Embeddings: Word2Vec, GloVe (static) vs. BERT (contextual). Implement one after your MVP.
Interview Question: "When would you use TF-IDF over Word2Vec?"
C. Modeling Start with classical ML (Logistic Regression, Naive Bayes), then move to deep learning (LSTM, BERT).
Interview Question: "Why might an LSTM outperform Naive Bayes for sentiment analysis?"
D. Evaluation Metrics: Accuracy, precision/recall (imbalanced data?), F1-score, confusion matrix.
Interview Question: "If 90% of your tweets are 'positive', is accuracy a good metric?"
- Iterate and Scale After your MVP:
Improve preprocessing: Handle negations (e.g., "not good") or emojis (😊 → "positive").
Try advanced models:
LSTM with Word2Vec embeddings.
Fine-tune a pretrained BERT (e.g., transformers library).
Deploy (optional but impressive): Use Flask/Django to create a web app that classifies tweets in real-time.
- Interview Prep: How to Leverage This Project Storytelling: Frame your project as a learning journey. Example: "I started with a simple Logistic Regression model (75% accuracy), then identified limitations like handling sarcasm. I upgraded to BERT, improving F1-score by 15%."
Challenges: Discuss roadblocks (e.g., noisy tweet data) and how you solved them.
Trade-offs: Compare techniques you tried (e.g., "TF-IDF was faster but BERT captured context better").
- Resources to Study Efficiently Books: Natural Language Processing in Action (hands-on NLP).
Courses: Fast.ai NLP or Coursera’s Natural Language Processing Specialization.
Libraries: nltk, spacy, scikit-learn, transformers (Hugging Face).
Key Advice Don’t over-study upfront. Learn just enough to unblock your next step.
Document your process: Keep a GitHub README explaining decisions (interviewers love this!).
Focus on intuition over math initially (e.g., "Word2Vec captures semantic meaning via neighboring words").
This approach ensures you learn by doing while building interview-worthy talking points. Good luck! 🚀