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The Power of Natural Language Processing: Transforming Unstructured Data into Actionable Insights

The Power of Natural Language Processing: Transforming Unstructured Data into Actionable Insights

Fri, 04/28/2023 - 5:46pm

It would be nice if all your company's essential data were in neat, easy-to-read spreadsheets. Unfortunately, most data is unorganized, difficult to interpret, and stored in various formats. Making sense of unstructured data can take hours, days, or weeks. Luckily, natural language processing tools can interpret large amounts of unstructured data in record time.

What Is Natural Language Processing (NLP)?

At this point, you may be asking yourself, “what is ‘NLP,’  or Natural Language Processing?” NLP is a branch of computer science aimed at giving artificial intelligence (AI) the ability to understand human language. Software that understands human language is nothing new; text-based games like Zork successfully interpreted words and sentences as early as 1977. Before that, an early chatbot called ELIZA was designed to mimic a psychotherapist speaking with a patient. However, these early examples didn't understand human language in context; instead, they used simple grammatical rules and text databases to mimic human conversation.
NLP technology combines computational linguistics and machine learning to create AI that processes human language closer to how humans do.

Using NLP to Transform Unstructured Data into Actionable Insights

Creating valuable insights with data begins with converting unstructured data into something manageable, whether a spreadsheet, chart, or graph.
NLP tools extract meaning from vast amounts of data, summarizing it in a way that's easy for humans to understand. Modern NLP programs are fine-tuned to follow specific instructions, meaning you can use them to generate a one-paragraph summation of a data set, create a pie chart, draft a “voice of the customer” program, and more.

Decoding Unstructured Data: What Is It?

Unstructured data is any data set that isn't arranged in a structured format. Many sources estimate that 80%-90% of global data is unstructured.

Three Common Types of Unstructured Data

1.    Social media posts:

If your brand uses social media to communicate with customers, there's a good chance you have tons of unstructured data points. Social media posts are an excellent way to gauge your customer's sentiments, as these sites have become a natural way for people to share their opinions.

2.    Customer service interactions:

Any time a customer calls, emails, or chats with your customer service team, numerous data points are generated. Unfortunately, collecting all the valuable data associated with these interactions is nearly impossible. NLP tools can be used alongside Internet of Things (IoT) devices, speech-to-text applications, and other technologies to extract meaningful information from customer service queries.

3.    Survey responses:

Many businesses regularly ask their customers and employees for feedback. Yes or no responses are easy to structure, but text inputs have loads of valuable data points that often remain unstructured.

Three Common Challenges Associated with Unstructured Data

1.    Identifying it:

Unstructured data exists in many forms. Before using it to steer your business's decision-making, you must understand the types of unstructured data your business, customers, and competitors create.

2.    Interpreting it:

Unstructured data spans various formats and may be contained in countless places. For example, some of your data sets could exist in physical documents, while other data points from the same set are contained in audio recordings.

3.    Using it:

According to Mark Twain, "data is like garbage. You'd better know what you're going to do with it before you collect it." Before you go through the trouble of collecting and interpreting your data, it's best to know how you plan to use it. While these plans will likely change upon reviewing the data, it can act as a compass as you decide what to do with your newfound insights.

Natural Language Processing Demystified

To truly harness the power of NLP for your business, you must know how it works. Luckily, it's surprisingly easy to understand when broken down into its core concepts.

Key Components of NLP

 

  • Tokenization: Breaking a body of text into smaller pieces, such as individual words or sentences.
  • Part-of-speech tagging: Analyzing each part of a text's speech to understand a sentence's meaning. For example, "the big red truck" would become "'article,' 'adjective,' 'adjective,' 'noun.'"
  • Stop-word analysis: The NLP tool removes common words, such as "a," "an," and "the," to focus on the essential words.
  • Lemmatization and Stemming: Breaking words into their root forms so the NLP technology can understand their meaning.
  • Sentiment analysis: This step involves understanding the emotion or tone of a body of text.

Four Ways NLP Can Enhance Your Business


1.    Chatbots:

NLP technology can be used to create chatbots, which can act as a business's first line of customer support.

2.    Sentiment analysis:

NLP programs can analyze social media posts, emails, and webpages to understand the author's tone.

3.    Text summarization:

These tools can summarize large bodies of text or data into a few easy-to-digest paragraphs.

4.    Market analysis:

NLP tools can analyze extensive data collections to understand business trends and opportunities.

The Power of Natural Language Processing

From sentiment analysis to machine translation, NLP is transforming how we interact with data. This technology offers many advantages when working with unstructured data and is likely already used by some of your competitors.

Why Use NLP for Processing Unstructured Data?

Traditional methods for processing unstructured data require human intervention, which is time-consuming and costly. However, machines can analyze large data sets almost instantly.

With techniques like sentiment analysis, NLP can determine the emotional tone of a piece of data to understand how people feel about a particular topic or product. Additionally, these tools can identify named entities, such as people, products, and locations, which helps track mentions of a particular brand or product.

Real-World Natural Language Processing Examples

 

  • Amazon Alexa: Virtual assistants like Alexa use NLP technology to convert spoken words into text, which is analyzed to decipher your request's meaning. These devices can often analyze specific sounds, such as a dog barking, a baby crying, or a window breaking, to understand when to activate connected smart home devices.
  • Microsoft 365 Copilot: Microsoft's line of Microsoft Office programs will soon use NLP to automate many daily tasks. You can use Copilot to visualize or interpret Excel spreadsheets, convert text commands into PowerPoint presentations, or summarize what was said in a virtual meeting.
  • ChatGPT and Bard: Chatbots like ChatGPT and Bard can answer questions, write stories, develop code, summarize text, and more.

What to Know Before Selecting an NLP Tool

GPT3, the large language model (LLM) behind ChatGPT, is one of the most well-known NLP tools. ChatGPT is free to use and has robust capabilities, but that doesn't necessarily mean it's the best choice for your business.

Natural Language Toolkit (NLTK) is one of the most popular frameworks for developing Python programs, making it an excellent choice for those who aim to create proprietary NLP programs for their business. CoreNLP is the Java equivalent of NLTK, letting you unlock robust sentiment analysis capabilities with a few lines of code.

Before choosing an NLP solution, consider the customization and flexibility you require. Some NLP tools can be trained to recognize specific industry jargon or language nuances, while others have limited customization options.

Beyond the Hype: NLP's Challenges

  • Ethical Considerations: Many NLP critics worry that the technology will put talented workers out of jobs. These tools could potentially replace customer service professionals, developers, writers, and more.
  • Data Quality Issues: OpenAI and Google are honest about the fact that their tools aren't always correct, and other NLP tools can be just as inaccurate. These inaccuracies aren't a big deal when using these tools for fun, but they can be problematic when using them to develop long-term business plans.
  • Limitations of NLP Technologies: NLP technologies struggle to interpret meaning in ambiguous text inputs, and they are inherently influenced by human bias since they're trained using human-produced content.

Be a Part of the NLP Revolution

University of the Cumberlands' data science program can equip you with the knowledge you need to become a data professional. In the modern world, this may mean understanding how to use NLP on behalf of a corporate client. Our hybrid program blends online coursework with in-class learning, making it an excellent choice for busy working professionals.

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