Sentiment Analysis Pipeline

You are an NLP engineer building a sentiment analysis system for a business. Based on the following requirements, design and implement a complete sentiment analysis pipeline.

Data Source: [PRODUCT REVIEWS/SUPPORT TICKETS/SOCIAL MEDIA/SURVEY RESPONSES/OTHER]
Volume: [NUMBER OF DOCUMENTS PER DAY OR TOTAL CORPUS SIZE]
Language(s): [PRIMARY LANGUAGE(S) OF THE TEXT DATA]
Granularity Needed: [DOCUMENT-LEVEL/SENTENCE-LEVEL/ASPECT-LEVEL SENTIMENT]
Domain: [INDUSTRY OR PRODUCT CATEGORY FOR DOMAIN-SPECIFIC UNDERSTANDING]
Output Requirements: [DASHBOARD/API/BATCH REPORTS/REAL-TIME ALERTS]

Build the sentiment analysis system across these six sections:

1. DATA COLLECTION AND PREPROCESSING
Design the data ingestion pipeline for the specified source. For social media, cover API access, rate limiting, and deduplication. For reviews, address scraping considerations and structured extraction. Implement text preprocessing including lowercasing, URL and mention removal, emoji handling which can carry sentiment signal so should be converted to text rather than removed, contraction expansion, and language detection for multilingual corpora. Build a preprocessing pipeline class in Python that chains these steps. Address the importance of preserving negation and intensifiers during preprocessing since they significantly affect sentiment.

2. LEXICON-BASED SENTIMENT ANALYSIS
Implement rule-based sentiment analysis as a fast, interpretable baseline. Use VADER for social media text and TextBlob for general text, explaining the strengths and limitations of each. Build a custom domain-specific lexicon by identifying words that carry different sentiment in the target domain versus general language. Implement aspect-based sentiment extraction by pairing opinion words with target aspects using dependency parsing with spaCy. Show how to compute document-level, sentence-level, and aspect-level sentiment scores. Provide code to visualize sentiment distributions and identify the most positive and negative aspects.

3. MACHINE LEARNING SENTIMENT MODELS
Build ML sentiment classifiers using both traditional and deep learning approaches. For traditional ML, implement TF-IDF vectorization with logistic regression and SVM, including proper text-specific preprocessing in the scikit-learn pipeline. For deep learning, fine-tune a pre-trained transformer model such as BERT, RoBERTa, or DistilBERT using the Hugging Face transformers library with the business-specific labeled data. Cover the labeling strategy for creating training data including annotation guidelines, inter-annotator agreement measurement, and active learning to minimize labeling effort. Compare model performance on precision, recall, and F1 for each sentiment class.

4. ADVANCED ANALYSIS FEATURES
Go beyond positive and negative classification. Implement emotion detection to identify specific emotions such as anger, frustration, joy, and surprise using fine-tuned models or zero-shot classification. Build a topic modeling layer using BERTopic or LDA to discover what customers are talking about and combine it with sentiment to create topic-sentiment matrices. Implement urgency detection for support-related text to flag critical negative feedback that needs immediate attention. Create sarcasm and irony detection heuristics for social media text where surface-level sentiment can be misleading.

5. AGGREGATION, VISUALIZATION, AND REPORTING
Design the analytics layer that transforms individual sentiment scores into business intelligence. Build time series sentiment tracking with trend detection and anomaly alerting. Create competitor comparison dashboards if monitoring public data. Implement automated insight generation that surfaces the most significant sentiment shifts, emerging negative topics, and positive highlights. Design executive-ready reports with sentiment distribution charts, word clouds weighted by sentiment, and representative example quotes for each sentiment category. Provide Plotly or Streamlit code for interactive dashboard components.

6. PRODUCTION DEPLOYMENT AND MONITORING
Architect the production system for continuous sentiment analysis. Design the API using FastAPI for real-time scoring and a batch processing pipeline for historical analysis. Implement model versioning and A/B testing of different sentiment models. Set up monitoring for model drift by tracking prediction distribution over time and alerting when it shifts significantly. Create a feedback loop where business users can correct misclassified sentiments to generate new training data. Address scalability considerations for processing high-volume text data including batched inference and GPU optimization.