How Sentiment Analysis Works

Move Beyond Positive, Negative, and Neutral Answers with People-Powered Sentiment Analysis

Businesses today want to know what their customers think about them. Are they happy with the customer service they received? Are they enjoying the new product? Many companies rely on sentiment analysis to identify and categorize opinions expressed in text. Most sentiment analysis tools search for keywords or strings of words and label them as “positive,” “negative,” or “neutral” through complicated linguistic algorithms. But language is complex, and algorithms and sentiment analysis tools have a hard time understanding the nuances.

CrowdFlower succeeds with sentiment analysis where automated solutions fail. Whether it’s misspellings, sarcasm, irony, or other variables, our human-in-the-loop platform consistently beats out-of-the-box natural language processors.  Our contributors interpret context clues to understand more than just “positive,” “negative,” or “neutral” results. Use our contributors’ judgments to build your training sets for your machine learning algorithms to get better insights about your brand, product, or campaign.

With CrowdFlower you will:

  • Train your machine learning algorithms: Get more accuracy from your training models with your own human-curated training sets.
  • Go beyond positive and negative: Jobs run through our fluent, qualified contributors for deeper, more actionable data, and you can ask follow-up questions to determine why people feel the way they do.
  • Analyze anything: Get opinions on any sort of content, including text, short videos, embedded images, and more.

Don’t get stuck with just “positive,” “negative,” or “neutral” answers. Learn how people-powered sentiment analysis can work for you and get started with CrowdFlower.