Learn Lighthouse, Natural and Screen-reader

A large number of people are unable to use the web effectively due to an impairment or disability.

As developers there are several tools and techniques we can use to make our web applications accessible, ensuring a great user experience that includes everyone.

We all agree that Accessibility is important. However, it’s a broad landscape and can be overwhelming figuring out where to start!

Erin Doyle is an expert in creating accessible React applications and has developed a course that will give you a concrete process for testing, refactoring, and building your applications with accessibility in mind.

After this course, you'll have a jump start on auditing and fixing accessibility issues in your applications and gain a better understanding of your target users and how to approach your web app design from their perspectives.


React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

Develop Accessible Web Apps with React

In this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

Identify the most important words in a document using tf-idf in Natural

screen-reader

There are many techniques for hiding content in user interfaces, and not all are created equal! Learn how different hiding techniques in HTML, CSS and ARIA impact keyboard and screen reader users in addition to visual display. As a bonus, we'll also take a look using a screen reader on a mobile device.

How Visible vs. Hidden Elements Affect Keyboard/Screen Reader Users

In this lesson, we’ll take a look at Natural’s phonetics feature. We’ll learn how to check whether two words sound alike, looking at both the SoundEx and Metaphone algorithms.

Check if words sound alike using Natural

This lesson introduces WordNet, which is an important resource in natural language processing. With WordNet, we can look up a word’s definition, or find its synonyms.

Find a word’s definition using WordNet in Natural

An important component of many natural language processing projects is being able to identify the grammar of a piece of text. We’ll learn how to do that with Natural’s parts of speech (POS) tagger.  

There are many tags, and it's worth looking them up online (search "POS tag symbols") to become familiar with them all. 

The setup of the tagger may seem a little strange, but it allows you to replace the lexicon or the rules with a different lexicon or rule set of your choice. 

Tag parts of speech using Natural

By this point we've seen that classification can take a long time, and with more data, it would take even longer. Luckily, Natural provides support to save your classifiers. In this lesson, we will learn how to save a classifier and load it into a new project in order to classify new data.

Using machine learning classifiers in a new project

A part of Natural Language Processing (NLP) is processing text by “tokenizing” language strings. This means we can break up a string of text into parts by word, sentence, etc. In this lesson, we will use the `natural` library to tokenize a string. First, we will break the string into words using `WordTokenizer`, `WordPunctTokenizer`, and `TreebankWordTokenizer`. Then we will break the string into sentences using `RegexpTokenizer`.

Break up language strings into parts using Natural

The Chrome Developer Tools (DevTools for short), are a set of web authoring and debugging tools built into Google Chrome.

chrome-devtools

What's in a name? In this lesson, I'll explain the concept of naming interactive elements for screen reader users, including forms, buttons, and links. You'll learn how to debug accessible names and descriptions using the Chrome Accessibility Developer Tools (previously a Canary experiment, now in Chrome), using multiple labeling techniques. We'll also listen to the effects of proper accessible names and descriptions in Voiceover and Safari.

For more information and the nitty-gritty browser implementation algorithm, refer to WAI-ARIA 1.1: 
- https://www.w3.org/TR/wai-aria-1.1/#namecalculation
- https://www.w3.org/TR/accname-1.1/

What is Accessible Name Calculation?

Landmark regions are used by screen readers to make it easier for users to navigate the content on the page by defining the structure of the page.  Tools like the Rotor in VoiceOver rely on landmark regions to provide users with the ability to skip directly to the desired section of the page.  We can do this either using HTML5 landmark elements or `<div>` elements with aria roles. In the past, the recommendation was to use both but now we should only use one or the other.

The following are ARIA roles for landmark regions:
* `banner`
* `complementary`
* `contentinfo`
* `form`
* `main`
* `navigation`
* `search`
* `application`

## References
* https://dequeuniversity.com/assets/html/jquery-summit/html5/slides/landmarks.html
* https://www.w3.org/WAI/GL/wiki/Using_ARIA_landmarks_to_identify_regions_of_a_page
* https://www.w3.org/TR/html-aria/#docconformance

Define Landmark Regions of a web page using ARIA roles

Using our accessibility auditing toolkit we can find issues concerning landmark regions in our web application.  The react-axe library will report findings in the JavaScript console of the browser.  We could also run the axe browser extension to see the same results.  The tota11y browser extension will annotate any landmark regions on the page and therefore if none are annotated it may indicate that they aren't any (_NOTE: tota11y will only annotate landmark regions using aria roles and not those using HTML5 elements_).  Finally using the VoiceOver Rotor in Safari will show us whatever landmark regions have been defined in the Landmarks menu. 

Test for Landmark Region Accessibility Issues in React

Landmark regions are used by screen readers to make it easier for users to navigate the content on the page by defining the structure of the page. Tools like the Rotor in VoiceOver rely on landmark regions to provide users with the ability to skip directly to the desired section of the page. We can do this either using HTML5 landmark elements or <div> elements with aria roles. In the past, the recommendation was to use both but now we should only use one or the other.  We should prefer HTML5 to ARIA roles when possible.

The following are HTML5 elements for landmark regions:
* article
* aside
* header
* footer
* main
* nav
* section

## References
* https://dequeuniversity.com/assets/html/jquery-summit/html5/slides/landmarks.html
* https://www.a11ymatters.com/article/intro-html5-sectioning-elements/
* https://www.w3.org/TR/html-aria/#docconformance

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