Text Analytics Tool For eCommerce Opinions
The technological breakthrough that empowers any role within the organization to access valuable granular, and digested consumer opinion data, right when they need it!
From Unstructured eCommerce Consumer Feedback Data
To Actionable Insights
Leveraging our expertise in machine learning, natural language processing, sentiment analysis, and big data – we’ve created technology that knows exactly what your customers mean when they’re talking about your brand, products or features.
eCommerce Big Data Collection
Whether it’s eCommerce reviews, social media engagements, your CRM, email correspondence, surveys, call center data, or a variety of other online and & offline sources. Our Analytics Engine can easily access and scan billions of data points every month.
Machine Learning & Sentiment Analysis
Applied o eCommerce Reviews from multiple sources, Revuze’ machine learning algorithms discover new aspects/topics in each product category – automatically and independently build a unique taxonomy for each.
This self-learning technology teaches itself about the world of your products, compares it with the industry benchmark and maximizes the value of the insights it provides
Our meaning-sensitive technology masters contextual understanding and cracks even the most misleading ambiguities. Featuring outstanding accuracy and performance even in the face of extensive data. Revuze recognizes multiple ways of referring to the same idea and identifies the right sentiment even in seemingly non-sentiment sentences.
Getting The Context
Revuze automatically categorizes, identifies, and extracts trends and topics from unstructured data – understanding context with exceptionally high precision and delivering truly actionable business insights. Revuze contextual intelligence understands topics and sentiment, regardless of the actual words customers use. With no manual keyword definition.
Identifying The Sentiment
Revuze sentiment analysis combines the power of computational linguistics, text analysis, and natural language processing to clarify subjectivity in customer perceptions. We filter customer attitude, recognizing contextual polarity and interpolating judgement, affective state, and intended emotional communication to create easy-to-understand and usable analysis.