The Machine Learning Natural Language Processing Snap Pack equips you with essential Snaps for transforming raw text into structured data that machine learning models can understand and analyze. Whether you’re analyzing customer feedback, processing support tickets, categorizing documents, or building text classification models, this Snap Pack provides the foundational text processing capabilities you need.
Break sentences into individual tokens for granular analysis, identify the most frequently occurring words to understand key themes and topics, and convert text into numerical representations that capture word frequency patterns—the critical first step for sentiment analysis, topic modeling, and text-based predictions. These Snaps handle the heavy lifting of natural language processing, letting you quickly move from unstructured text to actionable insights without wrestling with complex NLP libraries or custom code.
This Snap Pack has the following Snaps:
- Tokenizer: Converts sentences into an array of tokens.
- Common Words: Identifies the most common words and computes the frequency in which they occur.
- Bag of Words: Counts the frequency of the most common words in a text.
To learn more, please check out the documentation page.
