Optimizing Content Analysis Performance

Following up on our previous issue with the keyword density checker breaking on large text inputs, we've implemented initial fixes that prevent outright crashes when handling extensive content. While the tool no longer fails completely, we're now encountering severe performance bottlenecks specifically within the core text analysis modules when processing lengthy articles (10,000+ words).

This isn't a crash, but the processing times are becoming unacceptable for providing real-time feedback to users. The slowdown appears most pronounced during tokenization, n-gram generation (especially for longer n-grams), and subsequent frequency calculations for various on-page metrics. It seems highly CPU-bound and memory-intensive. Here’s a simulated console output illustrating the problem:

INFO: Processing document_id_12345.txt (12500 words)
DEBUG: Tokenization completed in 4.5s
DEBUG: Generating bi-grams completed in 12.1s
DEBUG: Generating tri-grams completed in 28.7s
DEBUG: Calculating keyword frequencies completed in 35.3s
ERROR: Total content analysis time: 80.6s - exceeding acceptable threshold.

My core question is this: What advanced optimization strategies or architectural patterns can be employed to significantly speed up content analysis for very large text inputs? We're looking beyond basic caching or simple parallelization of trivial tasks. Are there specific algorithms or data structures better suited for this at scale? Eagerly awaiting insights from anyone who's tackled similar performance challenges in large-scale text processing or SEO tools.