What is text analytics?
Text analytics is the automated process of translating unstructured text into data which can be analyzed for patterns, trends, and insights. This involves text mining, which is the process of pulling text from a source into a data analysis model, and natural language processing (NLP), which is the machine learning method of processing written language into data.
Why is text analytics important?
Text analytics is important for many different situations in which insights are desired from data in the form of unstructured text, or natural language. Some examples of this type of situation are search engines, social media analysis, customer review analysis, and other such scenarios.
Companies can benefit from sentiment analysis of social media mentions and customer reviews in order to gain insights on how customers feel about their brand and learn what changes they could potentially make to keep customers happy with their brand and product/service offerings.
In order to conduct these analyses, companies need machine learning models. Data insights cannot be gained from unstructured text without natural language processing. NLP is a commonly used type of ML model, meaning it’s relatively easy to create or find a template for, and is extremely useful in gaining data insights from textual data sources.
Text analytics and natural language processing
Natural language processing is the process of machine learning reading text or understanding speech by simulating the human ability to comprehend a natural language. While NLP algorithms don’t necessarily comprehend language, they are able to make sense of the words as data, by analyzing words as instances with relationships with one another.
The technology of natural language processing has become more mature over the past decade, due to products like Siri, Alexa, and Google’s voice search using this technology to understand and respond to user requests. In addition to voice assistants, sophisticated text mining technologies have also been developed for use in fields such as medical research, risk management, customer service, fraud detection, and marketing.
NLP systems today can analyze unlimited amounts of text data without fatigue, while practicing consistency and unbiased analyses. They are able to understand complex concepts and contexts to decipher the ambiguities of natural language and extract key facts and relationships.
There is a huge amount of unstructured data produced every day, by things like electronic health records or social media posts, making text analytics automation critical to analyzing data.
Enterprise text analytics
The use of text analytics at an enterprise level represents a real opportunity for companies to conquer challenges of data analysis across the whole organization. Many different departments within an enterprise can benefit from text analytics, such as customer service, product development, marketing and advertising, accounting and financing, and even more.
Effective natural language processing requires some features that should be incorporated into any enterprise text analytics solution. For example, many small-scale NLP algorithms would not hold up at an enterprise scale. This aspect of scalability is arguably the most important aspect of any machine learning application.