About Text Analytics
Text analytics is the process of transforming unstructured text documents into usable, structured data. Text analysis works by breaking apart sentences and phrases into their components and then evaluating each part’s role and meaning using complex software rules and machine learning algorithms.
Text analytics forms the foundation of numerous natural language processing (NLP) features, including named entity recognition, categorization, and sentiment analysis. In broad terms, these NLP features aim to answer four questions:
- Who is talking?
- What are they talking about?
- What are they saying about those subjects?
- How do they feel?
Data analysts and other professionals use text mining tools to derive useful information and context-rich insights from large volumes of raw text, such as social media comments, online reviews, and news articles. In this way, text analytics software forms the backbone of business intelligence programs, including the voice of customer/customer experience management, social listening, and media monitoring, and voice of employee/workforce analytics.
How Can Text Analytics Help Companies?
Colossal amounts of unstructured data are generated every minute – internet users post 4,56,000 new tweets, 510,000 new comments on Facebook, and send 156 million emails – so managing and analyzing information to find what’s relevant becomes a major challenge.
Thanks to text analytics, businesses are able to automatically extract meaning from all sorts of unstructured data, from social media posts and emails to live chats and surveys, and turn it into quantitative insights. By identifying trends and patterns with text analytics, businesses can improve customer satisfaction (by learning what their customers like and dislike about their products), detect product issues, conduct market research, and monitor brand reputation, among other things.
Text analytics has many advantages – it’s scalable, meaning you can analyze large volumes of data in a very short time, and allows you to obtain results in real-time. So, apart from gaining insights that help you make confident decisions, you can also resolve issues in a timely manner.
Explore the basics of how computers make sense of natural language. Learn how to harness the power of Text Analytics to gain further insight into your data.
With the rise of big data, the traditional strategy of analyzing text manually in order to understand the themes and patterns in our data has become slow and inefficient. For example, every day, an analytics team could receive thousands of online reviews to categorize, from hundreds of branch locations. It would be impossible to manually analyze each record and still keep up with analysis demands. For this reason, Text Analytics has become increasingly popular as a way to automate this process and to discover new patterns and trends that might have gone undetected otherwise.
Text Analytics, also known as Text Analysis or Text Mining, is the automated process of deriving important information from unstructured text data. It applies methods from several fields, such as Computational Linguistics, Information Retrieval, and Statistics, and has a variety of applications that are relevant to both business intelligence and scientific research. Data scientists in text-rich fields are turning to text analysis tools for help with customer surveys, vendor notes, call center interactions, medical records, industry-related research, legal documents, social media activity, and more.
Powered by natural language processing and statistical algorithms, Text Analytics tackles tasks such as Text Classification, Sentiment Analysis, Named Entity Recognition, and Relation Extraction. These tasks identify and extract important information from complex patterns in unstructured text, transforming them into structured data. This can allow companies to summarize opinions about products and services, connect specific medical symptoms with the effectiveness of different treatment plans, or even harness machine learning algorithms to inspect industry trends and effects of marketing campaigns.
By extracting all this information into structured data, analysts are able to quickly summarize and visualize trends in the data to gain important insights for making better business decisions or inspiring scientific discovery.