Natural Language Processing For Text Analytics

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. Many of the challenges in NLP are related to understanding human languages, i.e. enabling computers to derive meaning from natural language inputs.

Substantial growth in the volume and variety of data is due to the accumulation of unstructured text data which accounts up to 80% of all data. Establishments collect massive amounts of documents, emails, social media, and other text-based information, of which most of the data is unused and untouched.

Text analytics, through the use of NLP, is the key to unlocking the value within these vast data assets. In this era of big data, the right platform enables businesses to fully utilize their data lake and take advantage of the latest parallel text analytics and NLP algorithms. Text analytics facilitates the integration of unstructured text data with structured data to derive deeper and more complete depictions of business operations and customers.

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Thus, NLP is the scientific discipline that makes natural language accessible to machines while text analytics is the process of the extraction of useful information from text sources. As a result, NLP facilitates text analytics by establishing structure in unstructured text to enable further analysis. Text analytics covers tasks from annotating text sources to a wide range of models about the documents such as sentiment analysis, text clustering, and categorization. Text analytics can be deployed virtually across all sectors, namely in finance, insurance, media, legal, government, health care and retail industries, even in oil and gas.

The article introduces what NLP is and discusses the multidisciplinary nature of the field as it combines concepts and tools from computer science, artificial intelligence and linguistics focusing on extracting meaning from natural languages using computers and algorithms. The article discusses the rational for the need to engage in NLP and text analytics. NLP and text analytics are crucial capabilities as the vast majority of the data collected and stored by companies are unstructured and remained untapped. The distinction between NLP and text analytics is, as outlined in the article, NLP primarily makes natural languages accessible to machines while text analytics uses the platform to extract useful information from the text sources.

As to who and what area of business can benefit out of text analytics, the article elaborated that virtually every sector human activity can drive value from text analytics and even businesses and industries who seems more quantitative oriented can harvest significant business insights from using methods like sentiment analysis, text clustering, and categorization on customers. For instance, as mentioned in the article, text analytics is being used in the finance industry on compliance and fraud prevention; in the insurance sector in analyzing claims, billing, and adjuster notes text data and in the legal space to look through millions of unstructured documents consisting of emails, case files, court documents and health records.

In general NLP and text analytics are becoming critical assets to companies who are working to leverage data driven insights and decision making. The huge and diverse amount of unstructured data that companies are collecting and put in their data warehouses are remained untapped until they deploy the tools and techniques of NLP and text analytics. NLP and text analytics belongs to the future as they have the potential to transform to businesses to a level of analytical competitors.