Intel adds sentiment analysis model to NLP Architect
Supervised learning approaches, which rely on large data sets of annotated samples, handle domain sensitivity pretty well, but Intel notes that compiling the necessary corpora is labor- and time-intensive. That’s why their ABSA model is lightly supervised, meaning it’s able to ingest unlabeled text and output opinion and aspect lexicons after domain-specific lexicons are defined. All of NLP Architect’s models ship with end-to-end examples of training and inference processes and with supporting data pipelines, common functional calls, and other utilities related to natural language processing. They’re modularized for integration, and some of the components are exposed as APIs through Intel’s NLP Architect server, a platform designed to provide predictions across different models.
Intel adds sentiment analysis model to NLP Architect
Intel today revealed that as of version 0.4, NLP Architect includes models for a particular type of sentiment analysis dubbed aspect-based sentiment analysis (ABSA). Intel notes that it’s generally more accurate than the commonly deployed alternative, sentence-level sentiment analysis, which achieves high accuracy but fails to account for nuances within phrases (for example, when one aspect of the sentence is positive while another is negative). ABSA works by extracting aspect terms — words like “food” and “service” in the sentence “The food was tasty but the service was poor” — and determining their related sentiment “polarity” (i.e., whether they expressed positive or negative sentiment). For instance, an opinion that might be considered positive in the context of a movie review (e.g. “delicate”) may be negative in another (a cell phone review). One application it didn’t target was sentiment analysis, which involves detecting subjective information from text, but that’s changing courtesy a newly announced update.
