Natural language processing (NLP) is an artificial intelligence (AI) technique that helps a computer understand and interpret naturally evolved languages (no, Klingon doesn’t count) as opposed to artificial computer languages like Java or Python. Its ability to understand the intricacies of human language, including context and cultural nuances, makes it an integral part of AI business intelligence tools.

NLP powers AI tools through topic clustering and sentiment analysis, enabling marketers to extract brand insights from social listening, reviews, surveys and other customer data for strategic decision-making. These insights give marketers an in-depth view of how to delight audiences and enhance brand loyalty, resulting in repeat business and ultimately, market growth.

Read on to get a better understanding of how NLP works behind the scenes to surface actionable brand insights. Plus, see examples of how brands use NLP to optimize their social data to improve audience engagement and customer experience.

What is natural language processing?

NLP is an AI methodology that combines techniques from machine learning, data science and linguistics to process human language. It is used to derive intelligence from unstructured data for purposes such as customer experience analysis, brand intelligence and social sentiment analysis.

An image that defines natural language processing as an AI methodology that combines techniques from machine learning, data science and linguistics to process human language. It is used to derive intelligence from unstructured data for purposes such as customer experience analysis, brand intelligence and social sentiment analysis.

NLP uses rule-based approaches and statistical models to perform complex language-related tasks in various industry applications. Predictive text on your smartphone or email, text summaries from ChatGPT and smart assistants like Alexa are all examples of NLP-powered applications.

Deep learning techniques with multi-layered neural networks (NNs) that enable algorithms to automatically learn complex patterns and representations from large amounts of data have enabled significantly advanced NLP capabilities. This has resulted in powerful AI based business applications such as real-time machine translations and voice-enabled mobile applications for accessibility.

What are the types of NLP categories?

Using generative AI tools like ChatGPT has become commonplace today. So have business intelligence tools that enable marketers to personalize marketing efforts based on customer sentiment. All these capabilities are powered by different categories of NLP as mentioned below.

Natural language understanding

Natural language understanding (NLU) enables unstructured data to be restructured in a way that enables a machine to understand and analyze it for meaning. Deep learning enables NLU to categorize information at a granular level from terabytes of data to discover key facts and deduce characteristics of entities such as brands, famous people and locations found within the text. Learn how to write AI prompts to support NLU and get best results from AI generative tools.

Natural language generation

Natural language generation (NLG) is a technique that analyzes thousands of documents to produce descriptions, summaries and explanations. It analyzes and generates both audio and text data. The most common application of NLG is machine-generated text for content creation.

NLP in optical character recognition

NLP algorithms detect and process data in scanned documents that have been converted to text by optical character recognition (OCR). This capability is prominently used in financial services for transaction approvals.

How does NLP work?

According to The State of Social Media Report ™ 2023, 96% of leaders believe AI and ML tools significantly improve decision-making processes. NLP is what powers these tools.

Data visualization highlighting stats from The State of Social Media Report ™ 2023 that show 96% of leaders believe AI and ML tools significantly improve decision-making processes.

To understand how, here is a breakdown of key steps involved in the process.

  • Tokenization: Text is broken into smaller units such as words or phrases called tokens.
  • Text cleaning and preprocessing: The text is standardized by removing irrelevant details such as special characters, punctuations and upper cases.
  • Part-of-Speech (PoS tagging): NLP algorithms identify grammatical parts of speech such as nouns and verbs for each token to understand the syntactic structure of the text.
  • Text parsing: The grammatical structure in sentences are analyzed to understand the relationships between words.
  • Text classification: Text is classified into various categories using statistical models. Text classification powers various capabilities such as sentiment analysis and spam filtering.

Which are the top NLP techniques?

There are several NLP techniques that enable AI tools and devices to interact with and process human language in meaningful ways. These may include tasks such as analyzing voice of customer (VoC) data to find targeted insights, filtering social listening data to reduce noise or automatic translations of product reviews that help you gain a better understanding of global audiences.

The following techniques are commonly used to accomplish these tasks and more:

Data visualization that lists the top NLP techniques that assist marketing functions. The list includes: sentiment analysis, entity recognition, machine learning, semantic search, content suggestions, text summarizations, question answering and machine translations.

Entity recognition

Named entity recognition (NER) identifies and classifies named entities (words or phrases) in text data. These named entities refer to people, brands, locations, dates, quantities and other predefined categories. NER is essential to all types of data analysis for intelligence gathering.

Semantic search

Semantic search enables a computer to contextually interpret the intention of the user without depending on keywords. These algorithms work together with NER, NNs and knowledge graphs to provide remarkably accurate results. Semantic search powers applications such as search engines, smartphones and social intelligence tools like Sprout Social.

Machine learning (ML)

NLP is used to train machine learning algorithms to predict entity labels based on features like word embeddings, part-of-speech tags and contextual information. Neural networks in ML models depend on this labeled data to learn patterns in unstructured text and apply it to new information to continue learning.

Content suggestions

Natural language processing powers content suggestions by enabling ML models to contextually understand and generate human language. NLP uses NLU to analyze and interpret data while NLG generates personalized and relevant content recommendations to users.

A practical example of this NLP application is Sprout’s Suggestions by AI Assist feature. The capability enables social teams to create impactful responses and captions in seconds with AI-suggested copy and adjust response length and tone to best match the situation.

Sentiment analysis

Sentiment analysis is one of the top NLP techniques used to analyze sentiment expressed in text. AI marketing tools like Sprout use sentiment analysis to power several business applications such as market research, customer feedback analysis and social media monitoring to help brands understand how customers feel about their products, services and brand.

A screenshot of a Listening Performance Sentiment Summary in Sprout. It depicts the percentage of positive sentiment and changes in sentiment trends over time.

Text summarizations

Text summarization is an advanced NLP technique used to automatically condense information from large documents. NLP algorithms generate summaries by paraphrasing the content so it differs from the original text but contains all essential information. It involves sentence scoring, clustering, and content and sentence position analysis.

Question answering

NLP enables question-answering (QA) models in a computer to understand and respond to questions in natural language using a conversational style. QA systems process data to locate relevant information and provide accurate answers. The most common example of this application is chatbots.

Machine translations

NLP drives automatic machine translations of text or speech data from one language to another. NLP uses many ML tasks such as word embeddings and tokenization to capture the semantic relationships between words and help translation algorithms understand the meaning of words. An example close to home is Sprout’s multilingual sentiment analysis capability that enables customers to get brand insights from social listening in multiple languages.

How brands use NLP in social listening to level up

Social listening provides a wealth of data you can harness to get up close and personal with your target audience. However, qualitative data can be difficult to quantify and discern contextually. NLP overcomes this hurdle by digging into social media conversations and feedback loops to quantify audience opinions and give you data-driven insights that can have a huge impact on your business strategies.

Here are five examples of how brands transformed their brand strategy using NLP-driven insights from social listening data.

Social listening

NLP powers social listening by enabling machine learning algorithms to track and identify key topics defined by marketers based on their goals. Grocery chain Casey’s used this feature in Sprout to capture their audience’s voice and use the insights to create social content that resonated with their diverse community.

As a result, they were able to stay nimble and pivot their content strategy based on real-time trends derived from Sprout. This increased their content performance significantly, which resulted in higher organic reach.

A customer quote from Casey's social media manager saying how their content performance grew significantly after using Sprout Social

Topic clustering

Topic clustering through NLP aids AI tools in identifying semantically similar words and contextually understanding them so they can be clustered into topics. This capability provides marketers with key insights to influence product strategies and elevate brand satisfaction through AI customer service.

Grammerly used this capability to gain industry and competitive insights from their social listening data. They were able to pull specific customer feedback from the Sprout Smart Inbox to get an in-depth view of their product, brand health and competitors.

These insights were also used to coach conversations across the social support team for stronger customer service. Plus, they were critical for the broader marketing and product teams to improve the product based on what customers wanted.

Screeshot of Sprout's Listening tool showing metrics of Active Topics enabling brands insights on brand health, industry trends, competitive analysis and campaigns.

Content filtering

Sprout Social’s Tagging feature is another prime example of how NLP enables AI marketing. Tags enable brands to manage tons of social posts and comments by filtering content. They are used to group and categorize social posts and audience messages based on workflows, business objectives and marketing strategies.

Purdue University used the feature to filter their Smart Inbox and apply campaign tags to categorize outgoing posts and messages based on social campaigns. This helped them keep a pulse on campus conversations to maintain brand health and ensure they never missed an opportunity to interact with their audience.

Deriving qualitative metrics

NLP capabilities helped the Atlanta Hawks monitor qualitative metrics from social listening and get a comprehensive view of their campaigns.

The basketball team realized numerical social metrics were not enough to gauge audience behavior and brand sentiment. They wanted a more nuanced understanding of their brand presence to build a more compelling social media strategy. For that, they needed to tap into the conversations happening around their brand.

NLP algorithms within Sprout scanned thousands of social comments and posts related to the Atlanta Hawks simultaneously across social platforms to extract the brand insights they were looking for. These insights enabled them to conduct more strategic A/B testing to compare what content worked best across social platforms. This strategy lead them to increase team productivity, boost audience engagement and grow positive brand sentiment.

Monitor social engagement

NLP helps uncover critical insights from social conversations brands have with customers, as well as chatter around their brand, through conversational AI techniques and sentiment analysis. Goally used this capability to monitor social engagement across their social channels to gain a better understanding of their customers’ complex needs.

Using Sprout’s listening tool, they extracted actionable insights from social conversations across different channels. These insights helped them evolve their social strategy to build greater brand awareness, connect more effectively with their target audience and enhance customer care. The insights also helped them connect with the right influencers who helped drive conversions.

Harness NLP in social listening

In a dynamic digital age where conversations about brands and products unfold in real-time, understanding and engaging with your audience is key to remaining relevant. It’s no longer enough to just have a social presence—you have to actively track and analyze what people are saying about you.

Social listening powered by AI tasks like NLP enables you to analyze thousands of social conversations in seconds to get the business intelligence you need. It gives you tangible, data-driven insights to build a brand strategy that outsmarts competitors, forges a stronger brand identity and builds meaningful audience connections to grow and flourish.

Learn how social media listening can impact your business.