Reviewiz

Mass analysis of written reviews is difficult – but not for Reviewiz NLP automation

Reviews	NLP Processing
“Resolution was great (+) but awkward (-) to handle. Still, 4 stars.”	Split positives and negatives
“As advertised. 4 stars.”	Limited information
“OK camera but customer service was terrible. Will never buy another one from this brand again. Would give no stars if possible. 1 Star.”	Emotion-driven bad review also known as flame review
“I only buy Nikon cameras. Great choice. 5 stars.”	Biased opinion
“Tried this new company. Features are still not where they need to be but 4 stars for effort.”	Personal rating method based on perceived “effort”
“Tarible experience return prod”	Misspelling and abbreviation
“This camera’s quality is great, not. 2 stars”	New form of expression making for difficult interpretation of language

Reviewiz extracts meaning from reviews by leveraging many specialties

Machine learning

computational linguistics

unsupervised learning

natural language processing

sentiment analysis

DEEP LEARNING

artificial intelligence

FEATURE EXTRACTION

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Reviewiz Implements a Complex NLP Chain with Phased Text Analysis

Reviewiz cloud-based NLP executes many language processing operations to break down, analyze, and visualize a comprehensive summary of a large number of reviews in seconds

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