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.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.