Sentiment analysis

Reading every message to gauge mood is not realistic at volume. A Beam agent sends incoming text such as reviews or tickets to MonkeyLearn, applies the sentiment model your team chose, and writes the result onto the record so negative cases surface fast. Clear positive or neutral results are stored automatically. When the score sits near the boundary, the text mixes praise and complaint, or wording is sarcastic or ambiguous, the agent routes it to a person to judge, so an angry customer is never miscoded as content and quietly ignored.

Topic classification

Sorting free-text feedback into themes by hand does not keep up. A Beam agent passes each piece of text to a MonkeyLearn topic model, applies the categories your team defined, and tags the record so reporting and routing know what each item is about. Confident classifications are applied on their own. When text spans several topics, fits none of the defined labels, or the model returns low confidence, the agent holds it for a person to categorize, so your themes stay meaningful rather than filling with mislabeled entries over time.

Keyword extraction

Spotting the terms that matter across thousands of documents is slow by hand. A Beam agent feeds text to a MonkeyLearn extraction model, applies the rules your team set, and writes the pulled keywords or entities to the record so search and analysis have structure to work with. Clean extractions are saved automatically. When results look noisy, miss an obvious term, or a document is too unusual for the model, the agent flags it for a person to review, so downstream analysis is not built on unreliable tags.