Mankind has reached its peak of evolution and discovery. The consumer today looks for luxury and sophistication in the product and how it could benefit him or her in their daily life.
To sustain and stay at the top of the market and give absolute comfort to the consumers, business organisations are using different strategies and technologies. Natural Language Processing or NLP is one such technology penetrating deeply and widely in the market, irrespective of the industry and domains. It is extensively applied in businesses today and it is the buzzword in every engineer’s life. In short, NLP is everywhere.
So what is NLP? In simple words, NLP or Natural Language Processing, also known as computational linguistics, is a blend of language, machine learning & artificial intelligence (AI). It builds a technology which allows us to interact with machines as in normal human to human conversation. ‘Hey Siri’ on your iPhone or ‘Ok Google’ on your Android mobile are the products of Natural Language Processing.
A few years ago, we used to type keywords into Google search to get effective results. Today, you have the comfort of vocally seeking your help with this technology assistant. One of the most pragmatic tech trends, Natural Language Processing, has multiple applications in business today.
Some of the most important applications of Natural Language Processing for businesses in 2019:
Table of Contents
#1. Sentiment Analysis
Mostly used on the web & social media monitoring, Natural Language Processing is a great tool to comprehend and analyse the responses to the business messages published on social media platforms. It helps to analyse the attitude and emotional state of the writer (person commenting/engaging with posts). This application is also known as opinion mining. It is implemented through a combination of Natural Language Processing and statistics by assigning values to the text (positive, negative or neutral) and in turn making efforts to identify the underlying mood of the context (happy, sad, angry, annoyed, etc.)
This application of NLP helps business organisations gain insights on consumers and do a competitive comparison and make necessary adjustments in business strategies, whenever required. Such data is also useful in designing a better customer experience and enhancing the product. Furthermore, sentiment analysis or emotion exploration is a great way to know about brand perception.
We hear a lot about Chatbots these days, chatbots are the solution for consumer frustration regarding customer care call assistance. They provide modern-day virtual assistance for simple problems of the customer and offload low-priority, high turnover tasks which require no skill. Intelligent Chatbots are going to offer personalised assistance to the customer in the near future.
A lot of Industry analysts predict that Chatbots are an emergent trend which will offer real-time solutions for simple customer service problems. They are unquestionably gaining a lot of trust and popularity from the consumer as well as engineers. They are useful in providing standard solutions to common problems. Chatbots help save time, human efforts, cost and provide efficient solutions (and keep improving from learning) from time to time.
#3. Customer Service
Ensuring customer loyalty by keeping them content and happy is the supreme challenge and responsibility of every business organisation. NLP has aided in multiple functions of customer service and served as an excellent tool to gain insight into audience tastes, preferences and perceptions. Speech separation where the AI will identify each voice to the corresponding speaker and answer each of the callers separately. An excellent text to speech systems could even aid the blind. For example, a call recording of the customer can give insight into whether the customer is happy or sad, what are their needs and future requirements.
NLP could aid in translating the caller’s speech into a text message which could be easily analysed by the engineer. To sum up, this would be a great way to get to know the pulse of your audience.
#4. Managing the Advertisement Funnel
What does your consumer need? Where is your consumer looking for his or her needs? Natural Language Processing is a great source for intelligent targeting and placement of advertisements in the right place at the right time and for the right audience. Reaching out to the right patron of your product is the ultimate goal for any business. NLP matches the right keywords in the text and helps to hit the right customers. Keyword matching is the simple task of NLP yet highly remunerative for businesses.
#5. Market Intelligence
Business markets are influenced and impacted by market knowledge and information exchange between various organisations, stakeholders, governments and regulatory bodies. It is vital to stay up to date with industry trends and changing standards. NLP is a useful technology to track and monitor the market intelligence reports for and extract the necessary information for businesses to build new strategies. Widely used in financial marketing, NLP gives exhaustive insights into employment changes and status of the market, tender delays, and closings, or extracting information from large repositories.
These are some of the few applications of Natural Language Processing which will be witnessed by business organisations in the time to come. There are other applications as well, such as reputation monitoring, neural machine translation, hiring tools and management, regulatory compliance, data visualisation, biometrics, robotics, process automation etc. NLP is the key to the quest for general artificial intelligence since language is a key indicator of intelligence in our society.
The system behind the NLP concept is statistical in nature. For this concept to move from Natural Language Processing (NLP) to Natural Language Understanding (NLU) where the consumer can get to see and experience a human emotional connect with the machines, is the future prospect to work towards. Over the last decade, the information technology industry has taken its leap of faith and dug deep into the various aspects of the Natural Language Processing.
Business organisations have found, tested and executed most favorable applications of NLP to advance the progress of Business Intelligence. Yet, the technology needs lots of data and processes in place to understand, analyse and respond to the needs of the human mind.
Is a social science degree helpful for a career in NLP?
NLP is used to process what humans say in textual or auditory data and solve incoming requests from humans. NLP requires an extensive understanding of possible antecedents, and predictions of what humans speak are an aspect. Data Science and Machine Learning (ML) entail statistics and rigorous research methodology to produce accurate solutions. Analysts must know what might be causing an error and how it was formed. A background in social science entails cognisance of the human mind and communications, analytical thinking, learning data analysis, and standardised research methods, which can help in NLP. However, it won’t be enough to understand or practice Machine Learning and Deep Learning.
What is the difference between NLP and Deep Learning?
Natural Language Processing (NLP) utilises human language to build its Machine Learning models. NLP aims to understand how programs can analyse various human languages and process them to produce optimal responses. NLP hence has a specific niche in Artificial Intelligence. On the other hand, Deep Learning focuses on building neural networks for algorithms. Deep Learning is a part of Artificial Intelligence wherein it tries to analyse and update the algorithms to recognise and accurately react to information provided to them. NLP is a part of AI concerned with text and speech recognition, and Deep Learning provides optimised algorithms through Artificial Neural Networks (ANNs).
What programming languages are compatible with NLP?
Natural Language Processing (NLP) is a sub-field in Artificial Intelligence focusing on analysing human language to build Machine Learning models. NLP uses programming languages, statistics, computational linguistics mainly. Python is a user-friendly programming language compatible with NLP tasks; it displays semantics and syntax, making building NLP programs easier. Java is used for NLP due to its quick learning and straightforward interface. It has many open-source libraries and is an independent platform, making the building less complicated. While NLP can use Python to manage sentiment analysis and document classification, Java provides full-text search and image extraction services.