AI News, Natural Language Processing for Call Centers – What’s Possible and What’s Valuable
- On Sunday, September 30, 2018
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Natural Language Processing for Call Centers – What’s Possible and What’s Valuable
While it might be possible to quality check the calls of a single rep by manually listening to previous recordings, it isn’t realistic for a company to gain business insight from its wide array of recordings in this way.
Companies want to improve the performance of individual reps, but they also want to know: In this article we’ll assess four Artificial Intelligence capabilities offered by the leading text analytics company, Lexalytics (our partner for this article), specifically in the area of “Natural Language Processing” (NLP) These features are: Sentiment, Entity Recognition, Categorization, and Themes.
Companies used to need focus groups, or broad consumer / customer surveys to determine customer opinions – specifically asking “How do you feel about this?” Now, sentiment analysis allows companies to infer the vibe directly from these customer service interactions, getting the positive or negative straight from the customer in the moment where they are interacting with the company – without having to subject them to a post-call survey.
Configuration can be a single line, such as ‘Don’t interpret this particular sentence as negative.’ We got this information from billions of documents where we used a set of seed terms for positive and negative… and you can extract phrases and then analyze the distance vectors from those terms.
However, information only becomes actionable when it can be tied specifically to products, services, and other identifiable “entities.” Paul Barba, Chief Scientist at Lexalytics clarifies: “Cuts of lumber, types of cancer, variants of a stereo model – anything that a business considers an entity — can be identified and tagged as such.” Users looking to learn more about a specific entity (a competitor, a product, an event) can specify lists of entities ahead of time in order to track activity and sentiment around them.
With several different ways of categorizing content, from search queries to machine learning classifiers to Wikipedia-based categories, we provide the text analytics tools necessary to segment content exactly the way that is most relevant to any business.
Many representative examples of a “refund call” or “flight change call” or a “service request call” must be provided to an NLP engine in order to accurately detect the patterns that encapsulate that category.
- On Friday, January 18, 2019
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