Janak  Sapkota

Janak Sapkota

1618641585

NLTK Tutorial : Text Analysis

In this video, you will learn about the basics of text analysis using NLTK in python

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#nltk #python

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NLTK Tutorial : Text Analysis

Navigating Between DOM Nodes in JavaScript

In the previous chapters you've learnt how to select individual elements on a web page. But there are many occasions where you need to access a child, parent or ancestor element. See the JavaScript DOM nodes chapter to understand the logical relationships between the nodes in a DOM tree.

DOM node provides several properties and methods that allow you to navigate or traverse through the tree structure of the DOM and make changes very easily. In the following section we will learn how to navigate up, down, and sideways in the DOM tree using JavaScript.

Accessing the Child Nodes

You can use the firstChild and lastChild properties of the DOM node to access the first and last direct child node of a node, respectively. If the node doesn't have any child element, it returns null.

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");
console.log(main.firstChild.nodeName); // Prints: #text

var hint = document.getElementById("hint");
console.log(hint.firstChild.nodeName); // Prints: SPAN
</script>

Note: The nodeName is a read-only property that returns the name of the current node as a string. For example, it returns the tag name for element node, #text for text node, #comment for comment node, #document for document node, and so on.

If you notice the above example, the nodeName of the first-child node of the main DIV element returns #text instead of H1. Because, whitespace such as spaces, tabs, newlines, etc. are valid characters and they form #text nodes and become a part of the DOM tree. Therefore, since the <div> tag contains a newline before the <h1> tag, so it will create a #text node.

To avoid the issue with firstChild and lastChild returning #text or #comment nodes, you could alternatively use the firstElementChild and lastElementChild properties to return only the first and last element node, respectively. But, it will not work in IE 9 and earlier.

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");
alert(main.firstElementChild.nodeName); // Outputs: H1
main.firstElementChild.style.color = "red";

var hint = document.getElementById("hint");
alert(hint.firstElementChild.nodeName); // Outputs: SPAN
hint.firstElementChild.style.color = "blue";
</script>

Similarly, you can use the childNodes property to access all child nodes of a given element, where the first child node is assigned index 0. Here's an example:

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");

// First check that the element has child nodes 
if(main.hasChildNodes()) {
    var nodes = main.childNodes;
    
    // Loop through node list and display node name
    for(var i = 0; i < nodes.length; i++) {
        alert(nodes[i].nodeName);
    }
}
</script>

The childNodes returns all child nodes, including non-element nodes like text and comment nodes. To get a collection of only elements, use children property instead.

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");

// First check that the element has child nodes 
if(main.hasChildNodes()) {
    var nodes = main.children;
    
    // Loop through node list and display node name
    for(var i = 0; i < nodes.length; i++) {
        alert(nodes[i].nodeName);
    }
}
</script>

#javascript 

Autumn  Blick

Autumn Blick

1596584126

R Tutorial: Better Blog Post Analysis with googleAnalyticsR

In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.

On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.

But when it comes to comparing blog post performance, things get a bit trickier.

For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).

But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!

My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.

However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.

But then, I learned R, and realized that there was a much better way.

In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!

What we’ll need

To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.

Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!

You’ll also need the dyplrlubridate, and stringr packages installed — which, as a reminder, you can do with the install.packages() command.

Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:

post_url: the page path of the blog post

post_date: the date the post was published (formatted m/d/yy)

category: the blog category the post was published in (optional)

title: the title of the blog post (optional)

Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!

For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.

#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Hollie  Ratke

Hollie Ratke

1597989600

Text Analysis Within a Full-Text Search Engine

Full-Text Search refers to techniques for searching text content within a document or a collection of documents that hold textual content. A Full-Text search engine examines all the textual content within documents as it tries to match a single search term or several terms, text analysis being a pivotal component.

You’ve probably heard of the most well-known Full-Text Search engine: Lucene with Elasticsearch built on top of it. Couchbase’s Full-Text Search (FTS) Engine is powered by Bleve, and this article will showcase the various ways to analyze text within this engine.

Bleve is an open-sourced text indexing and search library implemented in Go, developed in-house at Couchbase.

Couchbase’s FTS engine supports indexes that subscribe to data residing within a Couchbase Server and indexes data that it ingests from the server. It’s a distributed system – meaning it can partition data across multiple nodes in a cluster and searches involve scattering the request and gathering responses from across all nodes within the cluster before responding to the application.

The FTS engine distributes documents ingested for an index across a configurable number of partitions and these partitions could reside across multiple nodes within a cluster. Each partition follows the same set of rules that the FTS index is configured with – to analyze and index text into the full-text search database.

The text analysis component of a Full-Text search engine is responsible for breaking down the raw text into a list of words – which we’ll refer to as tokens. These tokens are more suitable for indexing in the database and searching.

Couchbase’s FTS Engine handles text indexing for JSON documents. It builds an index for the content that is analyzed and stores into the database – the index along with all the relevant metadata needed to link the tokens generated to the original documents within which they reside.

An Inverted index is the data structure chosen to index the tokens generated from text, to make search queries faster. This index links every token generated to documents that contain the token.

For example, take the following documents …

The inverted index for the tokens generated from the 2 documents above would resemble this…

Here’s a diagram highlighting the components of the full-text search engine …

A Text Analyzer

The components of a text analyzer can broadly be classified into 2 categories:

  • Tokenizer
  • Filters

Couchbase’s engine further categorizes filters into:

  • Character filters
  • Token filters

Before we dive into the function of each of these components, here’s an overview of a text analyzer …

Tokenizer

A tokenizer is the first component to which the documents are subjected to. As the name suggests, it breaks the raw text into a list of tokens. This conversion will depend on a rule-set defined for the tokenizer.

Stock tokenizers…

Take this sample text for an example: “_this is my email ID: _abhi123@cb.com

A couple of configurable tokenizers…

  • Exception … This tokenizer allows the user to enter exception patterns (regular expressions) over the stock tokenizers.
  • Regexp … This tokenizer extracts text that matches the pattern (a regular expression) as tokens.

For example:

#json #couchbase #search #go #text analysis #full-text search #bleve #full-text #full-text-indexing

Daron  Moore

Daron Moore

1598404620

Hands-on Guide to Pattern - A Python Tool for Effective Text Processing and Data Mining

Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.

Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.

In this article, we will try and cover the following points:

  • NLP Functionalities of Pattern
  • Data Mining Using Pattern

#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm

Willie  Beier

Willie Beier

1596728880

Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

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#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials