Data-driven decision-making works. With data-focused companies exhibiting a 162% improved chance to outperform the competition, establishing a data-driven business culture has emerged as a top priority in 2022. Nevertheless, data containing potentially valuable insights come in many forms. Among these, human language data – in text and recorded speech – generally becomes difficult to analyze in volumes too large to read manually. To tackle this challenge, computer scientists have developed the field of natural language processing (NLP) to enable programs and machines to better understand inputs based on human language.
In this guide, you'll learn what NLP is and how it can radically enhance content marketing creation.
- Natural language processing (NLP) simulates processes in human language to enable programs to gain a deeper, nuanced and multilayered understanding of raw natural language inputs.
- Recent developments in NLP technologies for elastic and unsupervised machine learning tasks have opened up new applications for NLP in content marketing creation.
- With tools to extract topics and audience data such as sentiment and identity, NLP programs give marketing teams access to real-time actionable insights, helping them create more targeted and engaging content for their audiences.
What is natural language processing?
Natural language processing (NLP) is a field of AI development in computer science that focuses on writing and training AI programs to understand natural language input, from speech or text, through processes that simulate human language processing. From a programmer's perspective, NLP integrates computational linguistics—rule-based descriptions of human languages—with statistical, deep machine learning. The combination of fixed rules with the adaptive capacities of machine learning enables NLP programs to extract highly contextualized meanings from language inputs such as intent, inference and sentiment.
Many common technologies and programs, translation applications, speech-enabled interfaces such as Siri and Alexa and customer service chatbots rely on NLP. While these use-cases create different experiences for human users, they involve a shared set of fundamental NLP tasks.
Core NLP Tasks
Although we refer to different programming media as "languages," human languages radically differ from machine information processing in several critical ways. Human languages can:
- Express small quantities of information with nearly endless combinatorial variety.
- Contain complex ambiguities and irregularities that no programming language would tolerate, such as homonyms, homophones, metaphors, analogies and grammatical exceptions.
- Create highly contextualized meanings that depend on a shared understanding between speaker and listener, e.g., "Look who's here again."
Writing NLP programs capable of handling these challenges breaks down into several core tasks that commonly include:
- Grammatical Tagging: Identifies parts of speech, such as nouns, verbs and adjectives, in natural language
- Disambiguation: Semantic analysis to resolve words with multiple potential meanings, e.g., "Take him out" versus "I'd like to take you out sometime."
- Named Entity Recognition: Identifies the reference content of names for people, places and things
- Co-reference Resolution: Identifies the antecedents of pronouns – who we're talking about when we say 'he," "she," and "they" – and metaphors.
- Sentiment Analysis: Identifies attitudes and emotions that a speaker may have
- Speech Recognition: Converts voice data to text
- Natural Language Generation: Converts information into natural human language outputs
Applying NLP in content marketing
In recent years, content marketing has emerged as a strong use-case for NLP, enabling new capabilities and strengthening others. Here are four ways to apply NLP to create more engaging and effective marketing content.
1. Topic modeling
Topic modeling is an unsupervised machine learning technique for automatically extracting clusters of thematically related words and phrases – topics – from raw natural language inputs. AI developers characterize this method as "unsupervised" as it does not require any hard-coded lists of topics to find. Rather, NLP-enabled AIs can sufficiently disambiguate inputs to identify topics in the wild.
Topic modeling can change the game for marketing teams with access to large volumes of potentially insightful customer feedback—in social media comments, forum discussions, product reviews and customer service queries—but cannot manually sort and analyze the data. Passing that data through an NLP application for topic modeling gives your teams live visibility into what your customers are talking about and what content they are specifically looking for.
2. Sentiment analysis
In addition to semantic content, human languages also code sentiments that indicate, sometimes overtly, sometimes covertly, how the person speaking feels about a topic or a situation. However, extracting sentiment from raw language inputs requires multiple layers of assessment ascending from individual words to conversation length inputs. NLP applications enable sentiment analysis through algorithms of layered neural networks that resemble structures in the human brain.
In its simplest iteration, sentiment analysis focuses on distinguishing positive and negative sentiment in human language. Deeper iterations may break those general categories down further into emotions such as anger and disappointment or satisfaction and excitement. At either depth, sentiment analysis reveals how customers feel about what they're saying and may help you identify attitude trends in different channels of communication – such as email versus chatbot response – and areas where you may want to adjust the tone of your content.
3. Audience identification
The vocabulary, tone and style of natural language often indicate important information about the speaker. To learn more about who their audiences are, professionally, demographically and geographically, marketing teams can employ NLP parameters for audience identification in text mining programs.
With a deeper understanding of your audience and why they interact with your brand and content, you can create content tailored specifically to certain professional niches or regional issues. Extracting audience information from digital interactions will also grow in value as search engines, Google in particular deprecates third-party browser cookies that companies have previously relied on for audience data.
4. Keyword extraction
NLP can also help marketing teams align content with current SEO trends in search queries. Using analytical tools like Google Trends, you can compare extracted keyword shortlists from your current content and customer feedback data with global and regionalized trends to make keyword adjustments in your content creation pipeline. Drawing data from your customers and general browsing trends allows you to use different keyword sets to target new or recurring audiences.
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