Enhance Customer Experience With NLP

Understand Meaning in Context

Be Responsive to Sentiment

Impress Prospects Increase Revenue Improve Retention Rates

There are many examples where large amounts of descriptive information could be classified to make it more actionable and valuable.

For example, consider the millions of projects or bid opportunities that can be collected. They describe the project, product, or service that is in search of a vendor. In many cases, it’s impractical to read and understand all projects to find the few relevant to your needs. Classifying them in such a way that the universe of projects can be narrowed to those most relevant to you could make you exponentially more productive. A similar example using the same approach can be applied to analyzing vendor descriptions to classify companies in a vendor database by industry classification code.

Information may be captured to help inform a process where its meaning is not easily understood.

Consider the example to the left, a healthcare screening process that must operate with accuracy and completeness in spite of misspellings or language difficulties that can occur in rushed or busy environments. Machine learning can interpret the symptom information delivering highly accurate results and improving patient care.

Another example would be a high-volume customer support process where customers are constantly reporting issues, requesting help or asking questions about your products. Summarization algorithms help extract the main points from human generated text and categorize the information for easier consumption in a business process. With additional customized training, the algorithms can be modeled to extract data from the text and trigger other business workflows. Talk with us to learn more.

Otherwise known as opinion mining, sentiment analysis classifies text as positive, negative, or neutral based on specific keywords, phrases, or patterns of language associated with different sentiments. This approach is often used to analyze product reviews, social media posts, and customer feedback to gauge public opinion or sentiment about a particular brand or topic.

Aspect-based Sentiment Analysis organizes the text being analyzed into its different aspects. For instance, if you did a survey asking for feedback that could span multiple brands or products, and various topics, the analysis might look like the example to the right. Qualetics can detect sentiment from the text associating it with the brand and topic in real time to enable rapid response from your customer success team or to provide objective sentiment analysis to aid in marketing or communication-related decision-making.

When trying to understand someone’s sentiment (positive, negative, or neutral) isn’t enough, and you want to gain insight into how people feel about things, Emotion AI is necessary. Emotion AI is about understanding the emotions people are feeling. Analyzing emotion goes beyond analytics into more involved levels of NLP combined with machine learning. It can often include audio, image, and video analysis. The people’s reactions can be fed to the emotion AI model from the data(audio, image, and video) collected and it can further analyze what emotion they are portraying. Qualetics’ AI Management System combined with our ability to adapt and train Emotion AI models make it possible to deliver highly accurate results at scale.

In this example, Emotion AI was applied to a collection of comments. Comments could be from reviews for a product or movie or could be from a series of customer service posts. The text is analyzed to objectively reveal the emotions expressed. And if you were to analyze hundreds or even thousands of them en masse, they could be graphically presented across a valence arousal model as illustrated to the left. Similarly, if the information being analyzed is facial images or video, those technologies could be applied to distil emotional insights at scale.