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.
Try it yourself
Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.