Text Analysis Techniques
Text analysis extracts insights from text data.
Text Preprocessing
Lowercase, remove punctuation, tokenize. Remove stop words. Stemming/lemmatization.
NLTK, spaCy provide preprocessing functions. Regular expressions clean patterns.
Topic Modeling
LDA (Latent Dirichlet Allocation) finds topics. Gensim library.
n_topics parameter sets number. coherence score evaluates topics.
Sentiment Analysis
VADER: rule-based sentiment. TextBlob provides polarity/subjectivity.
BERT-based models provide state-of-art accuracy.
Key Takeaways
- Preprocessing is crucial for text analysis
- LDA discovers latent topics
- Sentiment analysis quantifies opinions