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.