Natural Language Processing (NLP) Models
To analyze off-chain textual data, such as customer feedback or social media activity, Trumnix integrates state-of-the-art NLP models.
Key Capabilities:
Sentiment Analysis: Detects sentiment in user reviews or community discussions to refine borrower profiling.
Entity Recognition: Identifies key borrower attributes (e.g., employer names, locations) in text data.
Topic Modeling: Categorizes textual data into relevant topics for better decision-making.
Protocols:
Data Cleaning Protocol: Removes noise (e.g., typos, spam) from input text to improve NLP accuracy.
Contextual Analysis Protocol: Applies transformers (e.g., BERT, GPT) for contextual understanding of textual data.
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