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  • August 7, 2019

Luminoso Adds to Daylight to Accelerate Customer Feedback Analysis

Luminoso has added sentiment analysis and an insights dashboard to Luminoso Daylight, its natural language understanding application for businesses to analyze unstructured text data, such as product reviews, open-ended survey responses, and support tickets.

"We built Daylight to offer all users, including those without data science backgrounds, the fastest, most intuitive way to automate the understanding of customer feedback datasets without barriers, such as the need for training machine learning models and creating ontologies," said Ying Chen, chief product officer at Luminoso, in a statement. "This focus has allowed us to attract a customer base of world-leading brands that are looking for better ways to understand emerging trends and new topics to improve the customer journey and drive market leadership."

Luminoso Daylight now features Highlights, a dashboard to help users find insights from customer experience data. With Highlights, employees in marketing, sales, product development, or service departments can upload customer feedback datasets related to specific products, services, or brands and immediately find answers to a number of questions, such as the following:

  • Which concepts in the feedback are most prevalent?;
  • What do people feel strongly about regarding a specific product, service, or brand?;
  • Which issues are affecting the star ratings of products or services?; and
  • What are the largest clusters of conversations?

Luminoso Daylight also now provides advanced document-level sentiment for concepts, identifying the words and phrases about which customers feel most strongly. Using Daylight's sentiment capabilities, users can do the following:

  • Understand the sentiment associated with words and phrases (negative, positive, and neutral);
  • Surface the top 50 words and phrases with the most sentiment;
  • Filter subsets of documents to examine differences in sentiment; and
  • Analyze sentiment natively in multiple languages, including emojis and emoticons.

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