Train your machine learning algorithms: Natural language models need training data. And the more specific that training data, the better. Words and bigrams that might be positive in one context can be negative in another, so out-of-the-box solutions can’t provide the accuracy you need. Training your sentiment algorithm with your own, human-curated training set means your models get smarter, faster.
Go beyond positive and negative: Because CrowdFlower sentiment analysis jobs run through a crowd of fluent, qualified contributors, you don’t need to stop with simple positive, neutral, or negative categories. You can ask follow-up questions to determine not just how people feel but why they feel that way. That means deeper, more actionable data your organization can really use.
Analyze anything: CrowdFlower can get you opinion on any sort of content. You can run traditional sentiment jobs with data like tweets, blog comments, or product reviews, but you can also embed images, short videos, and more.
Upload your data with a simple drag and drop.
Next, build your job. You can customize any part of the experience, from categories to follow up questions to nested logic.
Choose your settings and press launch! You’ll be able to monitor the job in real time.
If you’d like to dive deeper into how sentiment analysis on CrowdFlower works, here are a few resources to get you started: