Part of:

How Natural Language Processing Can Improve Business Insights

Why Trust Techopedia

Natural language processing allows for the processing and analysis of data not previously available, allowing for more in-depth insights.

As we are advancing rapidly in the computing and technology field, natural language processing (NLP) is becoming more relevant to businesses and enterprises. Natural language is nothing but what people are talking about in plain, simple language, in different electronic mediums like social networks, blogs, forums, etc. So, understanding and processing this natural language is known as NLP. The outcome of this processing is having a significant value to business, as it extracts the feelings, emotions and thought processes of the common users. Based on these insights, enterprises can take proper actions and increase their business value.

What Is Natural Language Processing?

Natural language processing (also sometimes called computational linguistics) is a field of artificial intelligence (AI) which dictates how a human being can interact with a computer without using machine language, but rather using natural human languages. The input can be taken in either written or spoken form.

For this to happen, humans must teach computers how they use and understand the languages that they speak. This is also one of the biggest challenges for NLP. An example of such a situation is a phrase in which words can have more than one meaning, like “baby swallows fly.” This can have two different meanings, which depend completely on the word which is being used as the verb (swallows or fly), which word is a noun (baby or swallows) or whether one is an adjective (baby). In the case of human beings, understanding the meaning depends on what the topic is and what makes sense within the context of the conversation.

Thus, for resolving this problem, the software must be programmed to understand the context or the topic and the structure of valid and invalid statements. Machine learning is a major part of NLP. AI can analyze the speech patterns of a user to easily understand the command given to it.

What Are Its Features?

The concept of NLP has brought about a storm in the modern technological world. NLP can be used to drastically simplify every interaction with computers with its many features. NLP can be used for analysis because of its immense language processing capabilities. It can also do deep analysis, which makes it very important in the fields of business, medicine and science. NLP can even be used for translating one language into another language easily, quickly and accurately. It also has data mining capabilities and can be used to extract a named entity with the help of its capability of entity recognition. Another feature of NLP is that it can automatically summarize huge amounts of text. All these features make NLP perfect for the business intelligence (BI) of a company.

There are thousands of features and benefits of natural language processing. NLP has all the necessary aspects that can help a company to mine useful information from huge amounts of data, provide better documentation and improve the efficiency of the processes for documentation.


Extracting Value for Business

Natural language processing, if used wisely, can really leverage the value of a company. The value of a company increases when the customer loyalty increases, and natural language processing can help the company do exactly that.

NLP can be used by the company for many techniques like sentiment analysis, which can help the company in gaining insight on the feelings of the customers when they are interacting with the company. This insight, when included with the insights gained from behavior prediction, can help the company provide the best services to the customers. This will increase customer loyalty for the company and the value of the company will increase automatically. (To learn more about sentiment analysis, see Social Chatter: Should Your Company Be Listening?)

The Relationship Between NLP and Text Analysis

Natural language processing has a component known as natural language understanding. This component, as its name suggests, mainly deals with the machine’s actual understanding of human language. While there are many uses of natural language understanding, one of the major applications is text analysis or sentiment analysis.

The need of text and sentiment analysis arose when companies began to realize that while data mining from transactional data was helping them understand more about the future actions of customers and the future market, they don’t actually know about the sentiments and the emotions of the customer during such transactions. This could lead to communication gaps and even prove to be a hurdle in the way of understanding the customers. Thus, businesses needed to know about the feelings of the customer, in order to gain their trust. (For more on data mining, see 7 Steps for Learning Data Mining and Data Science.)

Natural language understanding can be used for sentiment analysis from many different places. For example, these tools can search the internet for brand references and can tell you if these were negative, positive or mixed reactions. Another place from which useful insights can be gained is the email server of the company. NLP can be used to filter out the spam emails and keep only the useful parts. NLP is a very important part of text analysis, as it is derived from NLP itself.

Some Practical Use Cases

Many companies are using text and sentiment analysis for enhancing their customer base. Companies are using this for understanding more about the sentiments and the feelings of the customers after using their services. Some examples of such companies include Kia Motors, Best Buy, Intuit and Cisco Systems.

Even Paramount Pictures is using this system, in order to know about the quality of their movies and to understand the feelings of not only their customers, but any person associated with the company, including investors and employees of the company. Companies like Intel and IBM are also using this technology to get information about the sentiments of their employees.

What Is the Future Trend?

Companies are fiercely competing amongst themselves in order to get the most out of customers and provide them with the best services possible. In the future, this competition will only increase in magnitude, with new companies posing as competitors to the existing ones.

In this case, the NLP and text analysis will prove as important as ever. Such technologies will help companies gain an edge over competition easily.


Every day is a battle for businesses, a battle to race ahead of competitors, a battle to get the most customer support base and a battle to gain profits while providing the best services to customers. For this purpose, business intelligence can be a very important part of the company. One of its main duties is to help the company gain insights on the behavior of the customers, which further helps the company offer the best professional services.

While insights on the current customer behavior can be useful for predicting the future customer behavior, the analysis of the feelings of the customer can provide even more useful insights and can help a company decide if its services are good enough or not, and if not, what can be done to improve the quality of the services. While this concept is quite new, it is being adopted rapidly by many companies. This helps both a company and its customers, as the former gains a loyal customer base, while the latter receive the best-quality services.


Related Reading

Related Terms

Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…