Review analysis guide
Token compression for review analysis
Analyzing customer reviews with LLMs is powerful but expensive. Reviews are long, repetitive, and full of noise. SuperCompress keeps the sentiment-rich lines and drops the filler.
Review analysis with compression
from supercompress import Compressor
comp = Compressor()
def analyze_reviews(reviews, question):
# Combine all reviews into one context
context = "\n---\n".join(
f"Review {i}: {r["text"]}"
for i, r in enumerate(reviews)
)
result = comp.compress(context, question)
return llm.generate(question, result.compressed_text)Frequently asked questions
Does compression lose review details?
No. Only redundant or irrelevant review text is removed. Sentiment signals, specific complaints, and praise points are preserved.
Can I analyze thousands of reviews?
Yes. Compress batches of 20-50 reviews at a time against specific analysis questions.
Try it yourself
Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.