Natural language processing, aka NLP, is a broad and rapidly evolving segment of today’s emerging digital technologies often generalized as Artificial Intelligence (AI). Wikipedia defines NLP as “ a subfield of AI concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.” By harnessing NLP, AI can successfully imitate human speech, form naturally-flowing sentences and give human-to-machine interactions a personal touch.
Perhaps because both concepts are related to words and languages, natural language processing is often confused with text mining: advanced analysis technique used to filter large amounts of research and extract the relevant info. Text mining is more than just a search tool; its algorithms can understand complex concepts and identify patterns and trends across million of articles to provide valuable information with unmistakable novelty factor.
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The two concepts are, indeed, closely interconnected, with NLP being an integral part of text mining: the very feature performing semantic and grammatical structure analysis, and capable of understanding the sentiments behind the natural text. Natural language processing uses a variety of techniques to understand the complexities of human speech, and NLP software needs an extensive knowledge base to operate effectively.
In a nutshell, NLP is technology behind chatbots, virtual assistants, online translation services, and much more. Below are some examples of cross-industry use of NLP for a variety of business purposes.
By leveraging NLP, banks in developing countries can now assess the creditworthiness of clients with little or no credit history. Even if these clients have never used credit before, most of them still use smartphones, browse the internet and engage in other activities that leave a lot of digital footprints. NLP algorithms analyze geolocation data, social media activity, browsing behavior to derive insights into their habits, peer networks, and strength of their relationships. By analyzing thousands of client related variables, the software generates a credit score highly predictive of customer’s further activity. Access to customer info is only granted on consent and the data can never be transferred to third parties.
One of the examples of creditworthiness assessment tools based entirely on MLP and text mining is the Lenddo application developed by a Singapore-based company LenddoEFL. Lenddo has developed its patented technology based on 4 years of actual online lending experience that included collection, analysis and processing of billions of data points. Back in 2015, Lenddo opened its API for 3rd parties, such as banks, lending institutions, utilities and credit card companies worldwide to reduce risk, increase portfolio size, improve customer service, and verify applicants.
Neural machine translation
What has previously seemed like an awkward attempt to imitate the professional translation has now substantially improved, but neural machine translation (NMT)has taken the improvements even further. In 2016, Microsoft Bing pioneered the launch of neural machine translation technology; today, Google and Amazon are competing to deliver the best machine translation tools on the market.
Applied in neural machine translation, NLP helps educate neural machine networks. At this point, businesses can use machine translation tools to translate low impact content like emails, regulatory texts, etc. and speed up communication with partners as well as other business interactions.
These have actually been around for quite a while, back since 1966, but NLP paired with voice recognition technology has propelled them to an entirely new level. Today, chatbots can be easily confused with humans, as they are intelligent and can also recognize human emotions. Unsurprisingly, chatbots are increasingly used in business: in fact, Gartner predicts, chatbots will account for 85% of customer interactions by 2020.
Chatbots help meet customers’ request for personalization: by collecting user-relevant data they can address them individually and offer fully personalized experiences devoid of the stress of human-to-human communication. Moreover, chatbots increasingly find application in sales: they can target prospects, strike a conversation, schedule appointments and much more.
Chatbots already prove to deliver significant business value to companies that invest in this technology development. For instance, Asos reported being able to increase orders by 300% using FB Messenger Chatbot and enjoyed a 250% ROI while reaching almost 4 times more target users.
In its turn, Sephora was able to increase its makeover appointments by 11% via FB Messenger Chatbot.
Also read why AI-based chatbots are critical to driving enterprise value.
When it comes to adjusting sales and marketing strategy, sentiment analysis helps estimate how customers feel about your brand. This technology, also known as opinion mining, stems from social media analysis and is capable of analyzing news and blogs assigning a value to the text (negative, positive or neutral). A Switzerland-based company Sentifi uses natural language processing to find influencers and define its key brand advocates. Today’s NLP algorithms go as far as identifying emotions such as happy, annoyed, angry, sad. Needless to say, with precise tools like this marketers now have all it takes to develop actionable strategies and make informed decisions.
One of the leading European retailers was looking to harness the power of NLP and text mining for customer feedback sentiment analysis and hired a dedicated AI development team in Ukraine to build a solution. By adding this sentiment analysis tool, the company intended to increase customer loyalty, drive business changes, and achieve an appropriate return on sales and marketing investments. 8allocate, the company that hosted the retailer’s dedicated software team, performed data cleansing and munging as the primary task and applied words tokenization along with a Natural Language Toolkit that was used to define synonyms, semantics, and the overall tone of voice of feedback. Afterwards, the team implemented the business logic based on language peculiarities, abbreviations, collocations, and vernacular expressions, and completed a comprehensive semantic analysis.
This solution helped the retailer optimize and upgrade their marketing and sales strategy, which resulted in 30% revenue increase within 12 months of deployment.
Hiring and recruitment
By harnessing NLP, HR professionals can now considerably speed up candidate search by filtering out relevant resumes and crafting bias-proof and gender-neutral job descriptions. By using semantic analysis, NLP-based software sifts through the relevant synonyms to help recruiters detect candidates that meet the job requirements. Conversely, Textio app uses semantic categorization to tweak job descriptions in a way that would maximize the number of job applicants.
By analyzing digital footprint (i.e. social media, emails, search keywords, and browsing behavior) NLP helps advertisers identify new audiences potentially interested in their products. By performing the simple keyword matching routine, NLP software helps broaden the range of channels for ad placement, helping companies spend their ad budgets more effectively and target potential clients. Even though the sense ambiguation feature identifying in which sense the word is used in the given context is still far from perfect, this application of NLP has proved to bring the most value.
Keeping up with industry-related events is getting increasingly difficult these days. Simple media monitoring is no longer what marketers need: they need advanced tools to filter through millions of blogs, websites and social media posts to stay abreast of what’s going on in their industry. NLP software helps marketers stay informed of what their competitors are up to and be aware of the latest trends to finetune their strategies.
According to the Becker’s Hospital Review, there are 3 main use cases of NLP in healthcare:
Mainstay cases: speech recognition, clinical documentation improvement, data mining research, computer-assisted coding, automated registry reporting.
Emerging cases: clinical trial matching, clinical decision support, risk adjustment and hierarchical condition categories.
Next-gen cases: ambient virtual scribe, computational phenotyping and biomarker discovery, population surveillance.
Data mining integration in health IT systems allows healthcare providers and hospitals to reduce subjectivity in decision-making and provide a new useful medical knowledge. Predictive models used in medicine provide the best knowledge support ever and help develop a set of clear and reliable predictions for doctors to make better, informed decisions and improve prognosis, diagnosis, and patient treatment.
Once started, data mining becomes a continuous cycle of knowledge discovery, which helps any healthcare organization create a good business strategy.
To conclude, the number of applications of NLP in business is increasing at a head-spinning rate. Sentiment analysis, creditworthiness assessment, chatbots, market intelligence, advanced advertising, and hiring tools: NLP has all it takes to enhance customer service, accelerate time to market and boost revenues.
NLP based software currently permeates our personal lives as well and is certain to get more sophisticated in years to come. Gartner says, by 2020 we will have more interactions with chatbots than with our spouses. Apple Siri and Google assistant are the most spectacular examples of how we use NLP on a personal level. As for NLP application in business, these thought-provoking cases should give you the basic understanding of what natural language processing can do to maximize productivity, streamline operations, deliver insights, keep up with competition and derive value from routine processes.
What other NLP and text mining use cases in business would you add here?