Natural language processing (NLP) is an area of computer science and artificial intelligence 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.

A typical human-computer interaction based on NLP might go as follows:

• The human says something to the computer
• The computer captures the audio
• The captured audio is converted to text
• The text’s data is processed
• The processed data is converted to audio
• The computer plays an audio file in response to the human

NLP is everywhere even if we don’t realize it. Does your email application automatically correct you when you try to send an email without the attachment that you referenced in the text of the email? This is Natural Language Processing Applications at work. Although NLP applications rarely perform at a high level, they are already at work, helping us perform many of our daily activities.

While NLP may not be not as widely known as Big Data or Machine Learning, we use natural language applications or benefit from them every day. Here are some examples of the most widely used NLP applications:

Natural Language Processing Applications:
Machine Translation
As the amount of information available online is growing, the need to access it becomes increasingly important and the value of natural language processing applications becomes clear. Machine translation helps us conquer language barriers that we often encounter by translating technical manuals, support content or catalogs at a significantly reduced cost. The challenge with machine translation technologies is not in translating words, but in understanding the meaning of sentences to provide a true translation.

Automatic summarization
Information overload is a real problem when we need to access a specific, important piece of information from a huge knowledge base. Automatic summarization is relevant not only for summarizing the meaning of documents and information, but also for understand the emotional meanings inside the information, such as in collecting data from social media. Automatic summarization is especially relevant when used to provide an overview of a news item or blog posts, while avoiding redundancy from multiple sources and maximizing the diversity of content obtained.

Sentiment analysis
The goal of sentiment analysis is to identify sentiment among several posts or even in the same post where emotion is not always explicitly expressed. Companies use natural language processing applications, such as sentiment analysis, to identify opinons and sentiment online to help them understand what customers think about their products and services (i.e., “I love the new iPhone” and, a few lines later “But sometimes it doesn’t work well” where the person is still talking about the iPhone) and overall indicators of their reputation. Beyond determining simple polarity, sentiment analysis understands sentiment in context to help you better understand what’s behind an expressed opinion, which can be extremely relevant in undersanding and driving purchasing decisions.

Text classification
Text classification makes it possible to assign predefined categories to a document and organize it to help you find the information you need or simplify some activities. For example, an application of text categorization is spam filtering in email.

Question Answering
As speech-understanding technology and voice-input applications improve, the need for NLP will only increase. Question-Answering (QA) is becoming more and more popular thanks to applications such as Siri, OK Google, chat boxes and virtual assistants. A QA application is a system capable of coherently answering a human request. It may be used as a text-only interface or as a spoken dialog system. While they offer great promise, they still have a long way to go (take a look of thise video to see what happens when two spoken dialog systems talk to each other: https://youtu.be/WnzlbyTZsQY). This remains a relevant challenge especially for search engines, and is one of the main applications of natural language processing research.

Using natural language processing for creating a seamless and interactive interface between humans with machines will continue to be a top priority for today’s and tomorrow’s increasingly cognitive applications. (Source: www.expertsystem.com)

Until 2020, the amount of digital data produced will be around 40 zettabytes.Which means that there will be around 5,200 GB of data for every person on Earth (source IDC).

The majority of data then will be produced by machines while exchanging informaion over data networks. which would include e.g. data from sensors or smart devices. For now only a small part of the data being produced has been explored for its value through data analytics. IDC Research estimates that by 2020, 33% of all data will contain information that might be valuable if analyzed. In a quote from a IDC report in April 2014 it is been said that from 2013 to 2020 the digital universe is going to grow by a factor of ten. From 4.4 trillion gigabytes it is today to 44 trillion. It more than doubles every two years.

Companies in the top third of their industry, in the use of data driven decision making, are on average 5% more productive and 6% more profitable than their competitors. McKinsey showed that a typical marketing budget could be cut by 15 to 20% and still not lose marketing ROI. So these are some big effects and big impacts that marketing analytics can have on firm performance.

In 2018, data will continue to take the center stage for all marketing activities.
Find out who your target customers are, how to reach them and how to optimize operating models to meet their needs. Well executed marketing programs drive up both revenue and profits. Build the most effective customer segmentation capabilities and apply customer insights across your business.
I hope the above is useful to you. Do not hesitate to contact me at mike@michaelthomaswolff.com with any questions.