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 |
Machine learning
computational linguistics
unsupervised learning
natural language processing
sentiment analysis
DEEP LEARNING
artificial intelligence
FEATURE EXTRACTION
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