Lexalytics Simplifies and Improves Text Analytics for the Enterprise with New Machine Learning Capabilities - insideBIGDATA

For example, if you were to train solely on content without any view into how the system is making its decisions, that system might learn that the phrase "Greek bank" is negative, due to the deluge of negative stories associated with Greek banks over the years, even though the phrase is not inherently negative. This is a common problem with systems that attempt to analyze sentiment with a single model and will skew results over time. The Lexalytics HSDTrainer can consume any text corpus that has been appropriately marked up for sentiment, and then return a list of phrases and suggested scores for that text corpus, allowing analysts to both rapidly and transparently train sentiment. Emoji Analytics -- With Salience 6.2, social marketers can now analyze the meaning and sentiment of content that includes the latest emojis released in Unicode 9.0. For example, if a food manufacturer releases a new product that elicits social media posts with the new "nauseated face" emoji, Lexalytics can score the content as negative and alert the customer. Conversely, those same marketers can search for anything that mentions "nausea," and that emoji will return a hit.