Natural language processing (NLP) has come a long way in interpreting human beings. However, all languages have their own dialects and grammatical rules. Natural language processing helps in interpreting dialects of every language. 

How does it do this? A group of actors from different locations worldwide come together and speak different languages and dialects. Natural language processing tools will analyze these languages and dialects and allow them to be used for various purposes, such as receiving commands from a human being. NLP is used for voice technology that you use on your phone, car, and other places. 

However, has NLP changed the world for the better or not? What can we expect from NLP tools? Let’s find out! 

What is Natural language processing, and how does it work? 

Natural language processing (NLP) is the interpretation of human languages and dialects by machine learning. Nevertheless, each language has its own dialects and grammar. Some languages have more dialects, and others have fewer. For example, if you go to France, the language tone won’t sound the same in northern France as in southern France. 

Even though every language has a standard way of speaking by the book, it doesn’t mean everyone knows how to speak their language correctly but will do so based on how their dialect is. Thus, this is the power of NLP to interpret different dialects of languages. 

For example, if someone speaks Spanish and gives commands to their phone in Spanish, NLP allows voice technology to interpret the words in Spanish. Voice actors come together to add as many expressions and phrases as possible to a language in order for the NLP to process different dialects in that language. 

It’s important for your business, too, since you may be the one who is frequently using voice technology. For example, maybe you are concerned and ask yourself, ‘how does NLP work on Twitter sentiment analysis?’ or how you can speak in a different dialect and let the AI understand you. 

NLP is all powered through machine learning. Based on the important data you extract and collect, relying on machine learning allows you to improve NLP’s overall performance. 

The pros of NLP tools 

Performing large-scale analysis 

NLP technology allows you to conduct text analysis on all types of documents, such as emails, social media, online reviews, internal systems, and more. In addition, it can process large amounts of data in only a few seconds, or if the data is too large, it can do so in one or two minutes. Compared to conducting an analysis manually, this would take up to two weeks to complete! 

More accurate analysis and cost reduction 

It’s pretty standard for human beings to make mistakes whenever they go through repetitive tasks, and what’s excellent about NLP tools is that they are immune to these mistakes since they can be trained to not make any according to your requirements business

So, everything should be fine once you set everything up and program it to work based on your business requirements. Nevertheless, NLP tools don’t take a break when working, providing real-time feedback and working 24/7. 

Now let’s compare the costs to manual data analysis. With manual data analysis, you’ll need to hire more people to complete your analysis and have them work for days and weeks to finish it. However, NLP tools manage to do this without needing anyone, and instead of taking weeks to finish, they can complete this in a matter of seconds or minutes. 

Improves customer satisfaction 

NLP tools automatically sort and analyze customer service tickets based on urgency, sentiment, and other factors. Then, after they start dealing with customers, they’ll re-direct them to a suitable department or an assistant, so they are never left without the proper guidance. 

However, suppose you are super concerned about how happy your customers are. In that case, you can always run a customer satisfaction survey to help discover how satisfied your customers are while performing an NLP analysis. 

It helps you get actionable insights 

Whether you run an open-end survey or have to analyze online reviews and any other types of comments, they will always require a high level of analysis, which can be better understood by machine learning. NLP tools don’t make assumptions but are digging deep into in-depth analysis, real-world scenarios, actionable insights, and more.

The cons of NLP 

Can’t differentiate similar phrases 

If there’s one major disadvantage with NLP, it’s the challenges it faces with differentiation among similar words. By similar expressions, we mean words that sound the same but have different meanings. For example: ‘I scream and ice cream,’ ‘I ran to the grocery shop because I ran out of food,’ and more expressions. 

If you communicate these expressions to humans, they’ll quickly understand the point, but AI isn’t at that level yet, and we don’t know if it’ll ever be. While NLP has evolved to such a high point, it still struggles with differentiation. This can be problematic for answering questions of speech-to-text applications since they aren’t written in text. 


Some humor and sarcasm can be funny, but machine learning models aren’t evolved enough to process this. Even though many people may have seen jokes made with voice technology apps such as Siri, Cortana, machine models will always process things positively or negatively. 

Some expressions can be used by machine learning models, such as “whatever,” “Who cares,” and other expressions. However, if you use a regular expression sarcastically, the machine won’t be able to process it correctly. 

Speech errors 

Mis-spelled words or misused one can create a range of issues when analyzing text. Unfortunately, even though grammar apps and auto-correct can handle these types of mistakes, they can’t ever understand the writer’s intentions. 

Different dialects and mispronunciations can challenge the machine to understand what you are trying to say. However, as NLP evolves and is better trained by actors who join together and say expressions that the NLP will use, these errors will minimize over time. 

Lack of research 

NLP has come a long way for most commonly used, particularly in the most common languages. However, many parts of the world still haven’t provided enough data for NLP to analyze. For example, Africa is considered one of the poorest continents in the world but has more than 2000 languages worldwide. 

However, there isn’t enough data from each of these African languages. In North Africa, the most widely spoken language is Arabic, but in Central and the more southern parts, each district will have a different language. There are even countries in Africa that have several languages. 

For instance, only in Nigeria, there are more than 500 languages spoken! So people from the north don’t understand people from the south, and they’ll need to communicate in English to understand each other. 

Knowing this, there isn’t enough data available for NLP to process in these languages. Moreover, we don’t know if we’ll ever have enough data for NLP to process that many languages in Nigeria! 

Why is NLP so complicated? 

While NLP has many advantages and some disadvantages, certain things make NLP complicated, as we mentioned before: 

  • Overall context: NLP seeks to understand linguistic diction between words and expressions and converts them into a particular form that machines can interpret. Languages are complicated because each language has dialects, and dialects have their own expressions and meanings. Each dialect will have different words that are understood in a different context. 
  • Voice tone: As we mentioned before, it’s challenging for NLP to process and understand sarcasm. 
  • Too many languages: Human language isn’t easy to understand, but dealing with more than 6000 languages worldwide isn’t easy, especially following all of their linguistic rules! 
  • Uniformity: In order to process a language, we have to interpret it in a form that machines can understand. Machine learning (ML) algorithms can identify unstructured languages and convert them into forms that machines can understand. Thus, this is the stage where NLP collects data! 

Wrapping it up

That’s all for this article. This was everything on Natural language processing tools and what you can expect from them. Nevertheless, NLP has come a long way in development, but the obstacles it continuously faces in processing different types of expressions and being able to distinguish sarcasm and irony from serious conversations is what makes it hard to step to the next level. 

However, worldwide actors are coming together to try and minimize these issues. No matter how many languages the world has or how many dialects a language has, the more data we can gather and the more voices and expressions we can add, the better NLP will process it in the future. 

The future holds many opportunities in NLP, and one day, we might get the chance to communicate with AI and machines, similar enough to communicate with a human being