Text Classification

Sentiment Analysis

Understand people's emotions within large corpora of text.

Insights into your customer’s experience is one of the most crucial insights a product team can have. Sentiment Analysis is one of the methodologies to read from tons of reviews and messages. What are they happy about, what is important to them, what are they disappointed about?

Classifying such reviews can be quite difficult, as language is incredibly complex. Hence, your classifier needs plenty of training data. With kern, you have the right tool for this. Our system helps you to both scale your labeling to build large training datasets, and also improves existing labeled data via extensive data management capabilities. For instance, with help of high-level classifiers and third parties, your texts are analyzed for communication style, keywords or pre-classified with help of pretrained models. Active Transfer Learning is used in our application to integrate further implicit data-specific patterns, which are ultimately combined with our intelligent information integration. This way, you get full insights into your data, can improve the quality and quantity, and build better models.

Sentiment Analysis is already highly interesting, but shines the brightest when combined with other insightful predictions about your customer’s messages. For instance, combined with an Aspect Extraction model, you can not only understand how a client feels, but also about what exactly a client has such feelings. For instance, a text like “Their support simply is the best” can be categorized into “Positive” and “Support”. Aggregating these counts over all your messages, you can see where you shine, and where you need to improve.

Are you interested? Give it a try, kern comes with a free version. If you run into problems or have questions, we are more than happy to help you anytime!

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