Everything you Need to Know About NLP Analytics

Natural language processing, or NLP, is an exciting technology that holds the key to making sense of your ever-growing collection of unstructured data. Everyone from your customers to your employees are giving you data that’s critical for optimizing your operations and improving your business, but it remains locked behind a format that’s indecipherable for computers.

NLP acts as the translator between the human and computer, and powers NLP analytics solutions. Once the computer has a way of working with text data that it understands, it can process the data and surface a variety of insights.

 

How is NLP typically used?

Human speech may follow certain rules, but there are many deviations from that pattern that require AI-powered NLP to step in. Just think of how hard it would be to program in all of the exceptions to rules, slang terms, and other linguistic differences that emerge over time.

NLP uses machine learning as a way of gaining that understanding on its own. The system can adapt to language shifts over time and optimize itself for the type of unstructured data that your company commonly works with. You probably have a significant backlog of unstructured data that could use processing, so it’s not challenging to get the solution up and running with real-world data.

The NLP solution can identify notable trends in text, which is helpful for a range of use cases. You end up with an accurate analysis that can drive your data-driven decision making. It’s capable of quickly coding responses into different topic areas, so you can see at a glance what continually gets mentioned in the feedback.

 

What is NLP data?

One of the most common forms of NLP data is responses to open-ended questions. Since this text input doesn’t fall into a predefined response, the computer needs help finding out the true meaning and intent of the person. Since many decisions can be driven by emotions, sentiment analysis allows you to get inside the audience’s head to better understand their decision-making processes. Another form of NLP data is spoken language, commonly used for solutions that have speech-to-text functionality.

 

Why NLP analytics is important for businesses

You can’t set your organization up for long-term success by flying blind in the face of text data. With NLP analytics, you can:

  • Process unique responses to surveys without significant manual work involved: If you needed workers to manually go through each response, code it, and try to make sense of overall patterns, they could spend hundreds or thousands of hours on a single project. NLP analytics takes a fraction of the time to perform this functionality and eliminates human errors from the equation.
  • Discover what people think about your products and services on social media: Sorting through hundreds and thousands of social media comments can be a tiring process, and important data could remain hidden in it. NLP analytics allows you to get to the bottom of the responses, whether you’re managing a social media crisis or want to know what people are saying about your latest announcement.
  • Explore how people feel about new products and services: You don’t want to wait around for months to determine whether people enjoy your latest offerings. NLP analytics quickly gathers these insights so you can make decisions based on fresh responses.
  • Identify high-priority customers who are irate, upset, frustrated, or otherwise in a state where your customer experience isn’t living up to expectations: This audience segment has one foot out the door and they’re going to loudly proclaim that to anyone who’s listening in their social circle. You can recapture these customers by offering fast resolution, specialized resources, and sending the case to an escalation team.
  • Tap into employee feedback to identify areas of process inefficiency, potential investment, and other improvements: You can learn a lot about your business when you have the opportunity to hear from employees at all stages of the company. Your schedule might be too busy to individually talk to each person, but NLP analytics can take their open-ended responses and present you with findings on an on-going basis.
  • Quickly respond to sudden changes in the market driven by shifting sentiment: It’s hard to predict where a game-changing technology, product, or business is going to come from. In some industries, you can see shifts coming from 10 miles away. In others, it seems like the industry changes overnight due to startups. Speed up your reaction time by watching trends in your unstructured data.
  • Create a consistent structure for analyzing open-ended responses: Each person looking at verbatim responses and other unstructured data will have their own way of interpreting the information. You end up with inconsistencies that can muddy the data and make it less useful for analytics. Automated NLP analytics creates a consistent structure for evaluation, and changes to the model are applied throughout the system at the same time.
  • Reduce biases in text data analytics: Bias comes in many forms in data analytics, and it can lead to many problems with your data. While you can’t completely eliminate biases, as algorithm development and learning data both have the potential to introduce biases, you can reduce the effect they have on the end result.

These are just a few ways that NLP analytics enriches your business and fuels your growth.

 

Working with NLP analytics data

Once your NLP system finishes processing and analyzing your unstructured data, you can pull it into reports, run it through other big data solutions, visualize the data, and create custom data dashboards.

Combining NLP analytics data with other sources can provide a more complete view of the information that you’re working with. For example, if you combine open-ended question data from surveys with the associated customer profile in your customer relationship management platform, you get a deeper look at the way they think and feel during their interactions with your company, products, and services.

While a significant portion of the process is automated, humans still play an important role in NLP. The models used by machine learning text analysis are developed and maintained throughout the life of the software. As your business needs change, the algorithms that you use for your NLP solutions will also shift. For example, if you expand into new regional markets, you’ll want to make sure that regional dialects and slang are included. The machine learning models and quality source data are essential for getting the most out of an NLP analytics software, such as text mining tools. Learn more about the importance of text mining.

 

NLP analytics functionality

Each NLP analytics solution has its own feature set, tailored to the type of solution and the intended end user. However, this is a list of the most common features that are available in NLP analytics platforms:

  • Accurately detecting languages in responses: The system can pick up on the language in the response and use the appropriate model for working with it.
  • Translating and localizing text: Machine translation and localization take place on the backend, so the unstructured data doesn’t need to go through a different software before it’s translated.
  • Picking out parts of speech: Before the application can look at the deeper meaning of open-ended responses, it needs to look at the grammatical structure of the text. This step allows the NLP application to begin putting together the context and sentiment of each response.
  • Assembling a topic list: Quickly look over the common topics in the feedback. You can take a broad perspective or start drilling down into the data for a closer look.
  • Considering context when analyzing text: The NLP solution looks at the question that the person responds to, the channel that the response was made, and other important context clues that could color the data.
  • Surfacing named entities: If you want to see how often certain products, services, companies, and other named entities are mentioned, NLP analytics solutions support this use case. You have a lot of flexibility with this tool.
  • Digitizing and analyzing hard copy documentation: The paperless office hasn’t happened for many industries, and valuable information could be hiding on these pages. Optical character recognition, or OCR, software converts hard copies into digitized documents that can be edited and manipulated. NLP analytics solutions can look at this text for valuable information.
  • Providing appropriate assistance for customers without human intervention: Your audience might expect 24/7 responses but that doesn’t mean that it’s a feasible option for your company. Automated chatbots can leverage NLP tools to understand the context of the requests, as well as analyzing this unstructured data for details about your customers.
  • Automatically creating structured data out of unstructured data sets: Structured data has a countless amount of software that can analyze it and otherwise work with it. Once your unstructured data is turned into structured data, you have the capability to look at it with many types of tools.
  • Sentiment analysis: One of the biggest use cases for NLP is understanding exactly what someone means when they provide feedback, so it’s not surprising that sentiment analysis is frequently found in these tools.
  • Creating a document synopsis: Some companies have a vast content library but it’s not organized or structured in a meaningful way. NLP can create synopses for these documents, as well as identifying overall topics and categories that they should be sorted into.

NLP analytics will continue to be a valuable tool for getting the most out of your company’s data. By understanding the role of NLP analytics in working with unstructured data, you can capture the insights that are hidden in these data sets.

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