NLP Guide for Product and User Experience, Simply Explained



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NLP Guide for Product and User Experience, Simply Explained

Eight most important natural language processing (NLP) methods and their intersection with product management and UX/UI.

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Intersection of product management and artificial intelligence (AI) is a match made in heaven, inevitable, and will be empirically influenced by user experience design.

Natural language processing (NLP) is a product, especially for the purposes of building solutions that are user-centric.

You are a diamond in the rough if you have at least a shallow understanding across the wide spectrum of NLP while performing as a practitioner of user experience design.

Gaining proficiency in AI is no known endeavor, even for AI experts. AI is made up of subdomains in the 100s. While I found that almost all AI practitioners attempt to learn across the overall AI spectrum, all hone in to a specific subfield for professional experience.

The following eight NLP methods are recognizable to anyone who conducts NLP analytics end-to-end.

My goal with this post is to introduce the most important NLP methods practiced in AI right now and crosswalk at a high-level product management or UX/UI use cases to activate (to show the applications of theory to the practical) for those NLP methods.

Let’s get right to it.

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1. Tokenization

Tokenization is the process of taking a sequence of text and breaking it up into smaller pieces, known as tokens. Each token is typically a word or phrase but can be any size. This process is used by NLP programs to make large amounts of text easier to work with.

Applications in product management or user experience design

— Use tokenization to create a messaging bot to help end users communicate with the organization. By identifying the key tokens in a user’s input, these systems can provide better responses and fulfill user needs more effectively.

— Tokenize text data to create a more efficient workflow, like tokenizing user feedback to analyze and respond to common issues.

— Text can be generated automatically for input fields in a form that match the context of each field. As an illustration, if a user is asked to enter their “First Name,” we could apply a tokenization method to create an input field that only accepts alphabetic characters.

— For creating content blocks for a page layout. By tokenizing the content, we can easily control how much space each block takes up on the page and ensure that all content is properly aligned.

— Product managers could track user engagement with specific features or sections of a product interface. By assigning unique tokens to different areas of the interface, product managers could identify which parts of the product are being used (at what frequently) and where users might need more assistance.

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2. Stemming and Lemmatization

Stemming is the process to identify the root form of a word or reducing a word to its stem. For example, the stem of “jogging” is “jog,” and the stem of “climbing” is “climb.” Lemmatization is similar to stemming, and it also incorporates the meaning of a word in its root form. As a high-level illustration, the word “better” is also correlated with words like “good” and “great.” More precisely, an example of lemmatizing is the word “elephants,” which would also result in “elephant,” as “elephant” is the simplest form at the root of the “elephants” (and retains the same meaning).

Applications

— Improve search functionality on a website or app, making it easier for users to find the information they are looking for.

— Automatically generate tags for content, helping to organize and surface relevant information for users.

— Predictive modeling applications such as next-word suggestion or auto-completion, potentially providing a better user experience by reducing typing effort and increasing the accuracy of predictions.

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3. Part-of-Speech Tagging

This is when NLP assigns each word in a sentence a label based on its function in that sentence. So, “running” might be labeled as a verb, and “ran” might be labeled as past tense verb. The parts of speech include the following: noun, pronoun, verb, adjective, adverb, preposition, conjunction, and interjection.

Uses

— Automatically tag user remarks with the relevant part of speech, which can be used to improve the accuracy of NLP models.

— Identify common patterns in user remarks to inform design decisions.

— Automatically generate summary statistics about the parts of speech used in user remarks for analysis and reporting purposes.

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4. Named Entity Recognition

Named Entity Recognition (NER) is a process of identifying named entities in text and classifying them into pre-defined categories, like locations, organizations, and names associated with persons. Depending on the modeling pipeline you used, there may be dozens of built-in entities that come out of the box with it.

A tiny bit more detail: NER is applied (generally) to automatically extract information from text data sources to create structured data that can be utilized, especially for conducting sentiment analysis or building knowledge graphs from textual data.

NER Implementations

— User experience designers can integrate with NER to extract relevant information from text data, such as user reviews or feedback, leading to a potentially better understanding of their needs and pain points.

— Generate automatic reports that provide insights into product performance or trends over time.

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5. Sentiment Analysis

This is a process of analyzing text data to understand or identify opinions. An approach is looking at the words used and their context to determine if they are positive, negative, or neutral. Sentiment analysis can be conducted to understand user sentiment, especially for products or services.

Applications

-Gauge user sentiment around a product or feature to inform UX/UI or product decisions.

-Inform the development of bots or other digital assistants by understanding how users feel about interactions with them.

-Prioritize areas for improvement in a product by identifying which aspects are most likely to generate negative sentiment.

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6. Text Classification

A technique for placing text into various categories by looking at the words in the text and seeing which category they belong to. For example, let’s say you have a bunch of emails. You can train an algorithm to look for patterns in the emails that are labelled as “spam” or “not spam.” Then, when you receive a new email, the algorithm can look at it and decide whether it is likely to be spam.

Implementations

— Automatically categorize pieces of text according to topic to allow teams to quickly filter through a large amount of content and zero in on what they’re looking for.

— Identify the sentiment expressed in a piece of text, which can be helpful in gauging how successful or well-received a product is.

— Generate predictive models that suggest possible actions or outcomes based on descriptive (historical) data.

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7. Word Embeddings

A technique where each word is represented by a vector, which is generated using algorithms that take into account the context in which [2] the words appear. For example, the vector for the word “dog” might be different if it appears in a sentence about animals than if it appears in a sentence about transportation. Such associations allow for relationship between words to be captured and analyzed mathematically. As an illustration, one could find the vector that represents “husband” is closer to the vector that represents “wife” than it is to the vector that represents “cousin.”

Applications

— Summarize text passages (especially lengthier ones).

— Identify sentiment or emotional tone in written communications.

— Contextual spelling and grammar correction.

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8. Seq2Seq Models

Neural networks that take a sequence of items as input to produce a new sequence of items as output. For instance, you could use a Seq2Seq model to conduct translation between languages, or to generate text based on a sample of existing text.

Use Cases

— Inform the design of chatbots that can converse with users in natural language.

— Predict what a user is likely to type next to provide better autocomplete suggestions.

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