Learn Natural, Neo4j and Twit

In this course we will learn some of the basics of getting data into and out of Neo4J, as well as touch on some of the libraries and tooling available in the Neo4J community ecosystem, which we will leverage to quickly create and use a full-featured GraphQL API.

We will start by learning how to run and interact with Neo4J locally. Then we will develop a graph model in which to store the data that we pull from SWAPI via REST. We will then create an Apollo GraphQL API driven by a few simple schemas which fit into this model. We will then write some simple code to crawl SWAPI and insert the data into Neo4J through our GraphQL API. Once there is data in the store, we will learn about some of the features in `neo4j-graphql-js` that make connecting and accessing our graph Nodes easier.

Neo4j delivers the lightning-fast read and write performance you need. Neo4j combines native graph storage, scalable architecture optimized for speed, and ACID compliance to ensure predictability of relationship-based queries.

neo4j

Build a Neo4J & GraphQL API

In this course we’ll learn how to create Twitter bots. We’ll first learn how to interface with the Twitter API using Twit.js, which allows us to post statuses, search tweets, and interact with followers. We’ll create several example bots that show the diversity of possibilities, using powerful tools such as RiTa.js, Google’s Cloud Vision API, and Tracery. We’ll learn how to tweet data from Google Spreadsheets, and to compose and tweet a song for an audio bot. We’ll also learn how to host our bot logic on Heroku and Docker. 

Twitter API Client for node

Supports both the REST and Streaming API.

twit

Create your own twitter bots

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’ll get a real-time stream of tweets, and learn how to filter a stream by keyword, location, and user.

Use the Twitter Stream API with Twit.js

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

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

In this lesson, our bot will tweet hello to the Twitter world! We'll do this by using Twit's POST method to post our status to the statuses/update endpoint.

Post Statuses with Twit.js

In this lesson, we’ll create multiple functions to request, download, and save photos and data from NASA's API, and then have our bot upload these photos to Twitter and post them along with their descriptions. We'll also learn how to tweet videos using a video from NASA’s space archives.

Tweet Media Files with Twit.js

We’ll learn how your bot can get its list of followers, follow people, and look up friendships. We'll use Twit's GET method to get our followers at the followers/list endpoint, to get the users we follow at the friends/ids and friends/list endpoints, and to look up our friendships with the friendships/lookup endpoint. We'll also use Twit's POST method to follow someone at the friendships/create endpoint, and to send messages by posting to the direct_messages/new endpoint. 