NLP Data Analysis

Text is data with opinions, ambiguity, and mess. NLP analysis turns it into something countable: clean it, represent it, model it, and evaluate honestly — with error analysis, not just accuracy numbers.

Overview

The pipeline: define the question, collect and sample the text, clean it (with a light touch — over-cleaning destroys signal), represent it (from TF-IDF to embeddings to LLM features), model it (classify, cluster, extract), and evaluate on held-out data with error analysis. The question drives every choice: sentiment needs different handling than topic discovery, which needs different handling than entity extraction.

When to use

• Classifying text: sentiment, topics, intent, toxicity, urgency.
• Discovering structure: topic modeling, clustering, trend analysis over corpora.
• Extracting information: entities, relations, key phrases from documents.
• Analyzing language at scale: reviews, tickets, survey responses, social posts.

Core concepts

• Preprocessing: normalization (case, unicode), tokenization, and noise removal — calibrated to the task. Keep emojis for sentiment; drop them for topic modeling. Document every step.
• Representations: sparse (TF-IDF — interpretable, strong baseline), dense embeddings (semantic similarity, multilingual), LLM-derived (richest, most expensive). Start simple; complexity must earn its place.
• Supervised tasks: classification/extraction with labeled data — split properly, mind class imbalance, choose metrics that match the cost of errors (F1, not accuracy, for rare classes).
• Unsupervised discovery: topic models and clustering for exploration — validate topics by reading documents, not by coherence scores alone. Human judgment is the metric.
• Annotation: quality labels are the ceiling. Write guidelines, measure inter-annotator agreement, adjudicate disagreements. Bad labels guarantee bad models.
• Error analysis: read the mistakes. Confusion patterns tell you whether the problem is data, features, or the task definition itself.

Practical workflow

1. Define the question and the decision the analysis informs; sample the data representatively.
2. Explore: lengths, languages, noise, duplicates, class balance. Look at raw examples before any modeling.
3. Establish baselines: keyword rules or TF-IDF + linear model. Record the score to beat.
4. Iterate on representation and model, evaluating on a held-out set with appropriate metrics.
5. Do error analysis on 50+ mistakes: categorize them, fix the data issues, and only then tune the model.
6. Report with uncertainty: metrics plus confidence intervals, plus the error analysis summary and limitations.

NLP project checklist:
[ ] Question + decision defined; sample is representative
[ ] Preprocessing documented and justified per task
[ ] Baseline model + score recorded
[ ] Labels validated (agreement measured) if supervised
[ ] Held-out evaluation with task-appropriate metrics
[ ] Error analysis done; top failure modes addressed or noted

Common pitfalls

• Skipping the baseline: jumping to LLMs for a task TF-IDF solves. Baselines are cheap and often sufficient.
• Label leakage: information from the label in the features (e.g., the answer in the text). Always ask what the model could cheat with.
• Ignoring class imbalance: 95% accuracy on a 95%-majority class means nothing. Use F1, PR curves, per-class metrics.
• Over-cleaning: aggressive normalization destroying signal — casing, punctuation, and slang often carry meaning.
• Topic models without reading: trusting coherence scores over human inspection. Read the top documents per topic.
• No error analysis: reporting a single metric. The mistakes are where the insight — and the next improvement — lives.