Company’s state-of-the-art architecture identifies unique concepts within text-based communications, and analyzes the sentiment of each concept

Luminoso, the company that automatically turns unstructured text data into business-critical insights, unveiled its new deep learning model for analyzing sentiment of multiple concepts within the same text-based document.

“While sentiment analysis has been prevalent for well over a decade, the most common form of sentiment analysis today involves evaluating whether a document’s sentiment is overall more positive than negative,” said Adam Carte, CEO of Luminoso. “This type of analysis is overly-simplistic, as it fails to address nuanced comments such as customers explaining what they like and dislike about a product, or employee feedback about a company’s strengths and weaknesses. With Concept-Level Sentiment in Luminoso Daylight, businesses across industries will be able to upload any text-based document, and quickly receive a nuanced analysis of the author’s sentiment regarding the topics they wrote about.”

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Luminoso’s new deep learning model understands documents using multiple layers of attention, a mechanism that identifies which words are relevant to get context around a specific concept as expressed by a word or phrase. This model is capable of identifying the author’s sentiment for each individual concept they’ve written about, as opposed to providing an analysis of the overall sentiment of the document.

Added Joanna Lowry-Duda, Research Scientist at Luminoso, “Other companies have attempted to analyze sentiment at the concept level, but they use unreliable, hard-to-maintain sentiment word lists. With Luminoso, customers benefit from our deep learning model that automatically creates a complex representation of the concept and its context to find sentiment across any industry.”

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Using Concept-Level Sentiment, users will be able to:

• Effectively analyze mixed feedback — Concept-level sentiment analysis is critical for capturing and understanding the voice of the customer (VoC). For example, product reviews rarely contain just one type of feedback, and it’s important to tease apart the good from the bad. Getting a polarity for each of the topics in an open-ended survey response is critical for understanding what works and what doesn’t for your customers.
• Quickly surface buried feedback — Uncovering negative comments in overwhelmingly positive open-ended survey responses is critical for better understanding customers and employees. For instance, in voice of the employee (VoE) surveys, employee feedback can be overwhelmingly positive and delivered in an upbeat way in an effort to soften criticisms. Concept-Level Sentiment in Luminoso enables users to quickly identify and understand “buried” feedback, such as negative points in an overwhelmingly positive HR survey.
• Intuitively aggregate concept sentiment across an entire dataset — For instance, after responses to a mobile app market research survey are loaded into Luminoso Daylight, a user can get a distribution of positive, negative, and neutral opinions about every aspect of the mobile experience across all of its mentions in the dataset.
• Analyze customer and employee feedback across multiple languages — Global organizations often receive customer and employee feedback in multiple languages. With Luminoso, users can analyze the sentiment of concepts, natively in 15 languages.

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