Leveraging the Power of Natural Language Processing for Data Interpretation


The mere thought of a computer program that can converse just like us humans may sound absurd. But if that’s the case, then absurdity might be our new reality – well, in the case of technology at least. This nascent technology is also known as natural language processing (NLP), an emerging field of artificial intelligence. It’s worth noting that NLP is more than just a simple chatbot. It’s no Siri and it’s not like Alexa either. It’s a whole different chatbot that’s based on language models. 

And like with any innovation, professionals across industries have tested the waters on this popular branch of artificial intelligence. Before we discuss its uses, let’s dive deeper into the true meaning of natural language processing. 

What Is Natural Language Processing? 

Natural language processing, a rapidly-developing branch of artificial intelligence, is the technology that acts as a bridge between human language and computer language. In a way, programmers use NLP for computer programs to understand our language, in order to perform more complex and automated tasks. Translating texts, summarizing content, and even interpreting data are notable examples of its applications. 

That being said, the development of natural language processing was not at all easy. The main problem it needs to overcome is human language itself. After all, the computer wasn’t initially invented to understand the nuances and intricacies of language. Human language is more than just about the words – it’s the meanings that it conveys. 

How Can You Leverage the Power of NLP in Data Interpretation?

It may have taken a while to get here, but NLP now has the power to interpret data in novel ways. From collecting them to analyzing complex data sets, NLP has forever changed the way we interpret data. Here’s how you can leverage the power of NLP in data interpretation: 

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Identify the Problem 

First things first, you have to identify the problem. That way, you’ll have an idea of how you want to use the program. For example, do you want it to analyze large data sets of text sent by customers through online chat and identify the keywords associated with them? NLP makes it easier for chatbots to determine what a customer needs. Is it a question about your refund policy? Or how they can track your order? With the help of AI, chatbots will know the right thing to say even when the same message is spun in different languages from different customers. 

Another example is the analysis of huge chunks of text on social media. A digital marketing agency can benefit from the content analysis feature of NLP tools to identify trends. This enables them to create a data-driven content strategy that increases their social media presence. It all starts with identifying the problem. Without it, you wouldn’t be able to use NLP to its fullest potential. 

Data Collection 

Of course, collecting data is crucial for data interpretation, and even more so when using NLP algorithms. Data collection isn’t as simple as just using your data in its rawest form. For NLP algorithms to analyze them better, you’ll have to clean your data. This can be done in various ways: using lowercase letters, removing unnecessary information, and omitting special characters. Following these practices ensures that the algorithms are accurate and efficient. Additionally, it guarantees that its analysis is relevant. 

Train the Algorithm 

This is how it works. The machine must first understand what the user said. Then, it must determine the action it needs to take. Once this is done, it reacts to what the user said. For example, a user asks, “Can you provide me a SWOT Analysis of Google?”. Once the algorithm understands the user’s request, it reacts by providing the strengths, weaknesses, opportunities, and threats of Google. 

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Training your algorithm to understand your data is dependent on the data that you provide. It’s helpful to split your data into two sets: the training set and the testing set. The training set is the data you use to feed your algorithm so that it can analyze your texts. Meanwhile, the testing set is used to evaluate the performance of the NLP algorithm. 


Testing your algorithm isn’t complete without evaluation. As mentioned previously, we use testing sets to evaluate the performance of our algorithm. If, say, the NLP algorithm doesn’t meet the required standards, you can search for another one that suits your needs. Another option is to change the parameters to further increase its accuracy. 

In Conclusion

Testing new NLP algorithms takes time. You’ll first have to identify the issues you want to solve, collect the necessary data, and then clean them. Afterward, you’ll have to train the algorithm and evaluate if it’s accurate enough for your needs. But once you get it right, you’ll be surprised how far this new technology can take you when interpreting data. Whether you need to summarize content, analyze them for its intent, or search for important keywords in blocks of content – this innovation has got you covered. And with the rapid growth of artificial intelligence, it’s only expected that this is just the beginning of something great.

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