Problem-specific
Sentiment analysis compression
Sentiment analysis with LLMs sends customer feedback text and analysis instructions. Compression removes filler content while keeping the sentiment-bearing phrases.
Sentiment analysis pipeline
from supercompress import Compressor
comp = Compressor()
def analyze_sentiment(reviews):
for review in reviews:
# Keep sentiment signals, remove filler
result = comp.compress(review, "What is the sentiment?")")
sentiment = llm.generate(
f"Sentiment of: {result.compressed_text}"
)
yield {review.id, sentiment)Frequently asked questions
Does compression affect sentiment accuracy?
Minimally. SuperCompress preserves sentiment-bearing phrases and removes neutral filler.
Can I analyze thousands of reviews per dollar?
Yes. With 65% compression, your dollar processes ~3x more reviews.
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.