keyword density for content optimization

hey everyone,

just launched our keyword density & frequency checker tool, and it's mostly solid for typical use cases. but, we're really hitting a wall with larger text inputs, specifically when users try to optimize content that's really long.

the core problem is our tool struggles big time with performance and accuracy when analyzing content over, say, 5000 words. this is pretty crucial for effective content optimization, especially for long-form articles or detailed guides. our current methods are just falling short, leading to timeouts or incomplete analyses.

we’re currently using a custom regex-based tokenizer combined with a pretty standard stop-word filter. it works fine for smaller chunks of text, but when we feed it large documents, we see significant slowdowns and occasional, kinda unpredictable, memory spikes. also, getting accurate results for multi-word keywords and handling all the stemming variations consistently across large texts is a real headache. we need to be precise for good content optimization.

we've tried a few things: optimizing the regex patterns, experimenting with different tokenization libraries (even a stripped-down NLTK version for speed), and rudimentary text chunking. the chunking, however, often breaks the context needed for calculating multi-word keyword density correctly, which defeats the purpose. it's like whack-a-mole; fix one thing, break another.

here's a simplified pseudo-code snippet showing where we often hit bottlenecks in our main processing loop:

function analyze_text(large_text):
    tokens = custom_regex_tokenizer(large_text)
    filtered_tokens = filter_stopwords(tokens)
    word_counts = {}
    multi_word_counts = {}

    for i in range(len(filtered_tokens)):
        word = filtered_tokens[i]
        # Basic single word count
        word_counts[word] = word_counts.get(word, 0) + 1

        # Attempt at multi-word keyword detection (simplified example)
        if i + 1 < len(filtered_tokens):
            bigram = filtered_tokens[i] + " " + filtered_tokens[i+1]
            multi_word_counts[bigram] = multi_word_counts.get(bigram, 0) + 1
        # ... similar for trigrams, etc.

    # This loop gets super slow with 5000+ words, especially the multi-word parts
    return calculate_densities(word_counts, multi_word_counts)

so, i have a few specific questions for you pros out there:

• what are the most performant and accurate text parsing techniques for keyword density on very large documents? are there specific algorithms or data structures that shine here?

• any recommendations for open-source libraries or algorithms (python/node.js preferred, but open to anything) designed for high-throughput text analysis in this context?

• how do you efficiently handle stemming, lemmatization, and multi-word keyword detection without absolutely killing performance on large inputs?

• are there any distributed processing patterns (like using something akin to MapReduce principles) that make sense for this specific kind of content optimization task, or is it overkill?

really appreciate any insights or pointers to help us nail this. help a brother out please...