#sentiment-analysis

Fine-tune BERT model for custom sentiment analysis. Steps: 1. Data preprocessing (tokenize, pad, mask). 2. Load pre-trained BERT model (Hugging Face Transformers). 3. Define custom classification head. 4. Configure optimizer (AdamW) and scheduler. 5. Implement training loop with validation. 6. Handle class imbalance (weighted loss). 7. Evaluate metrics (F1-score, accuracy). 8. Quantize model for inference efficiency. Include usage example.

Build comprehensive NLP pipelines for text analysis, sentiment analysis, and language understanding tasks. Text preprocessing pipeline: 1. Data cleaning: remove HTML tags, normalize Unicode, handle encoding issues. 2. Tokenization: word-level, subword (BPE, SentencePiece), sentence segmentation. 3. Normalization: lowercase conversion, stopword removal, stemming/lemmatization. 4. Feature extraction: TF-IDF (max_features=10000), n-grams (1-3), word embeddings (Word2Vec, GloVe). Traditional NLP approaches: 1. Bag of Words: document-term matrix, sparse representation, baseline for classification. 2. Named Entity Recognition: spaCy, NLTK for entity extraction, custom entity types. 3. Part-of-speech tagging: grammatical analysis, dependency parsing, syntactic features. Modern approaches: 1. Pre-trained transformers: BERT (bidirectional), RoBERTa (optimized BERT), DistilBERT (lightweight). 2. Fine-tuning: task-specific adaptation, learning rate 5e-5, batch size 16-32. 3. Prompt engineering: few-shot learning, in-context learning, chain-of-thought prompting. Sentiment analysis: 1. Lexicon-based: VADER sentiment, TextBlob polarity scores, domain-specific dictionaries. 2. Machine learning: feature engineering, SVM/Random Forest classifiers, cross-validation. 3. Deep learning: LSTM with attention, BERT classification, multilingual models. Evaluation metrics: accuracy >80% for sentiment, F1 score >0.75, BLEU score for generation, perplexity for language models.