Most available data can be classified as unstructured and includes everything from business documents, to emails, news articles, images, video, audio, and social media posts. Gartner estimates that over 90 percent of all digital data is unstructured.

Natural Language Processing (NLP), a field of Artificial Intelligence (AI), analyzes, understands, and derives meaning from unstructured data. NLP is used for Named Entity Recognition (NER) and Sentiment Analysis, which we will discuss further below, as well as Automatic Text Summarization, Parts-of-Speech tagging, and more.

Named Entity Recognition (NER) is a natural language processing technique that uses machine learning to identify named entities in text data and classifies them into one or more predetermined categories. Entities can be names of people, organizations, locations, topics, interests, and more.

Named Entity Recognition is used to automatically categorize news articles, customer feedback, customer support tickets, social media posts, and resumes.

Natural language-based named entity recognition enables you to effectively search unstructured data, to improve process efficiency, and create better personalized customer experiences.

Sentiment Analysis is a form of text mining with the objective to extract, classify, and understand the feelings, opinions, or meanings expressed by customers, employees, or other stakeholders.

It is used to detect positive or negative sentiment in, for example, social media posts, customer feedback, and news articles.

Sentiment Analysis helps to identify what drives customer satisfaction and dissatisfaction, customer intentions, and how customers feel about certain topics.

Sentiment Analysis is of great value for anticipating market trends, identifying new business opportunities, learning about the competition, and improving brand, product, and service experience.