5 Business Uses for Natural Language Processing

We find ourselves fighting against an overwhelming data deluge, accelerating all the time with the world’s data expected to doubleevery two years for the next decade, hitting 45,000 exabytes in 2020. A big portion of this data is unstructured, composed of images, text, speech, and videos coming from a variety of sources. From a business standpoint, the most fundamental and important piece of unstructured data is text, and organizations have the potential to harness these text data assets, which are derived from both internal and external channels. 

How can organization take advantage of this unstructured data? By utilizing Natural Language Processing (NLP). Through a set of techniques, NLP can be used to extractinteresting patterns within these different forms of text and other unstructured data, which can be of significant business value. For example, businesses are inundated with information rich documents such as business contracts, product documentation, pricing playbooks, and marketing campaigns, to name a few. As these processes and operations evolve and mature, businesses also needto derive trends and patterns to gain insight, usually either extracting structure and meaning from a text document or identifying relationships and content similarities that exist. 

Tappingthe power of NLP can define what steps are to be carried out to generate those meanings, relationships, and existing similarities within blocks of text. To accomplish this however, businesses also must determine where to apply NLP. 

Business leaders should explore the following five business uses for Natural Language Processing:

1.     Brand Sentiment Analysis: Understanding the emotional tone of consumer social posts in public domain, knowing the trending opinion and having a real-time view of the customer’s pulse is a critical element of brand marketing. NLP helps to derive these insights from textual data. 
2.     Recruitment: Semantic search of resumes to filter the best fit is far more powerful than keyword match. NLP is at the backbone of such targeted selection and recruitment methods. 
3.     Media and Publishing: Publishing companies deliver news and content after aggregating and curating from a variety of sources. The process of aggregation is far more accurate for the reader’s preferences with NLP-based selection. 
4.     Financial Markets: With market conditions shifting daily, analysts need real-time and relevant content at their fingertips, and NLP can be an answer to provide such content more efficiently and accurately to influence timely decision making. 
5.     Call Center Operations: High volumes of consumer interaction creates the need for a critical capability to prioritize which tasks to act upon first. Using voice to text, NLP and machine learning can more quickly deliver insights to the most important customer inquiries. 

Over and above the fact that data in the form of text is available in abundance, textual data is significant for businesses to take advantage of, because it usually delivers a lot of information in a small package. When looking at a typical email or service request for example, there are specific elements that must be identified that provide crucial information such as customer name, products in use, issues, and suggested actions significant for organizations to recognize in order to maintain positive relationships with customers.

It’s feasible through machine learning and analytics to extract meaningful patterns in textual data using techniques in NLP. These techniques stem from text normalization – lowercasing, removing stop words and punctuations, and lemmatizationwithin textual data – and have strong statistical and mathematical underpinnings. An organization can also consider using an application of NLP known as Conceptual Search. In Conceptual Search, text processing, language engineering, concept relationships, and search and information retrieval are layered, to enable gleaning of the insights from the different data elements a block of text can offer.

Natural Language Processing is building in stature and utilizing its techniques provide decisive information which may not have been previously practical with only traditional means. As businesses become even more saturated with exabytes of data, NLP could be the solution that sets a new standard for textual data insights. 

Karthikeyan Sankaran is Director of Data Science and Machine Learning at LatentView Analytics, a marketing technology and decision science analytics firm. 

To learn more about AI and NLP, TMC (News - Alert) invites you to attend The Future of Work. The event will take place Jan. 30 through Feb. 1 in Fort Lauderdale, Fla. For more details, visit: http://www.futureofworkexpo.com/

Edited by Maurice Nagle

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