Abigail  Cassin

Abigail Cassin

1596570840

AI and Text Analysis: Best Approaches To Follow

Artificial intelligence and text analysis provide you with a deep understanding of your business’s performance and customers, empowering you to make better decisions.

From automating repetitive tasks to delivering actionable customer insights, AI helps businesses to improve revenue and user experience. Similarly, text analysis interprets extensive collections of data to uncover consumer trends and opportunities.

Text analytics refers to the method of analyzing a text to extract useful, high-quality information. Around 80-90% of the data in every organization is unstructured. Text analysis uses AI and ML technologies to generate valuable insights, which you can use to make data-driven decisions.

The tremendous amount of data generated each day provides businesses with an opportunity and a challenge.

  • The opportunity: It allows companies to get in-depth insights on your customer’s opinions about your products or services.
  • The challenge: Processing a vast amount of data and generating valuable information from them.

Text analysis helps businesses overcome this challenge and make the most of this opportunity.

Text Analysis Techniques

Previously, text analysis was performed manually, which involved using keyword dictionaries and identifying recurrent terms. As a result, companies had to wait for months before getting actionable insights.

Thanks to the advancement in technologies, you can now process a massive amount of data in no time. Here are the techniques that are used in text analysis.

  • Artificial Intelligence (AI): Artificial intelligence refers to the technology that imitates human behavior concerning the intelligence processes involved in problem-solving.
  • Natural Language Processing (NLP): A part of AI, NLP enables the computer program to review and understand human languages. Text analysis uses NLP to eliminate the noise from unstructured data to help you understand customers’ opinions about your business and identify trends.
  • Machine Learning (ML): A subset of AI, ML, can automatically learn from past experiences and improve itself without any manual intervention. ML categorizes new pieces of data by analyzing how the old ones were processed.
  • Deep Learning (DL): A part of machine learning, DL can process and use data to better understand the context in the unstructured data, thereby improving the accuracy of automated analysis of the text.
  • Sentiment Analysis: It refers to the ability of a computer system to determine how customers feel about your business, products, or services (positive, neutral, or negative).

#artificial intelligence (ai) #text analysis #text classification

What is GEEK

Buddha Community

AI and Text Analysis: Best Approaches To Follow
bindu singh

bindu singh

1647351133

Procedure To Become An Air Hostess/Cabin Crew

Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

 

Physical and Medical standards –

  • Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
  • The candidate's body weight should be proportional to his or her height.
  • Candidates with blemish-free skin will have an advantage.
  • Physical fitness is required of the candidate.
  • Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
  • There should be no history of mental disease in the candidate's past.
  • The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!

You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

So That I may recommend, joining Special Personality development courses, a learning gallery that offers aviation industry courses by AEROFLY INTERNATIONAL AVIATION ACADEMY in CHANDIGARH. They provide extra sessions included in the course and conduct the entire course in 6 months covering all topics at an affordable pricing structure. They pay particular attention to each and every aspirant and prepare them according to airline criteria. So be a part of it and give your aspirations So be a part of it and give your aspirations wings.

Read More:   Safety and Emergency Procedures of Aviation || Operations of Travel and Hospitality Management || Intellectual Language and Interview Training || Premiere Coaching For Retail and Mass Communication |Introductory Cosmetology and Tress Styling  ||  Aircraft Ground Personnel Competent Course

For more information:

Visit us at:     https://aerofly.co.in

Phone         :     wa.me//+919988887551 

Address:     Aerofly International Aviation Academy, SCO 68, 4th Floor, Sector 17-D,                            Chandigarh, Pin 160017 

Email:     info@aerofly.co.in

 

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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 

AI and Text Analysis: Best Approaches To Follow

Artificial intelligence and text analysis provide you with a deep understanding of your business’s performance and customers, empowering you to make better decisions.

From automating repetitive tasks to delivering actionable customer insights, AI helps businesses to improve revenue and user experience. Similarly, text analysis interprets extensive collections of data to uncover consumer trends and opportunities.

Text analytics refers to the method of analyzing a text to extract useful, high-quality information. Around 80-90% of the data in every organization is unstructured. Text analysis uses AI and ML technologies to generate valuable insights, which you can use to make data-driven decisions.

The tremendous amount of data generated each day provides businesses with an opportunity and a challenge.

  • The opportunity: It allows companies to get in-depth insights on your customer’s opinions about your products or services.
  • The challenge: Processing a vast amount of data and generating valuable information from them.

Text analysis helps businesses overcome this challenge and make the most of this opportunity.

#artificial intelligence (ai) #text analysis #text classification

Eve  Klocko

Eve Klocko

1596380168

AI and Text Analysis: Best Approaches To Follow

Artificial intelligence and text analysis provide you with a deep understanding of your business’s performance and customers, empowering you to make better decisions.

From automating repetitive tasks to delivering actionable customer insights, AI helps businesses to improve revenue and user experience. Similarly, text analysis interprets extensive collections of data to uncover consumer trends and opportunities.

Text analytics refers to the method of analyzing a text to extract useful, high-quality information. Around 80-90% of the data in every organization is unstructured. Text analysis uses AI and ML technologies to generate valuable insights, which you can use to make data-driven decisions.

The tremendous amount of data generated each day provides businesses with an opportunity and a challenge.

  • The opportunity: It allows companies to get in-depth insights on your customer’s opinions about your products or services.
  • The challenge: Processing a vast amount of data and generating valuable information from them.

Text analysis helps businesses overcome this challenge and make the most of this opportunity.

Text Analysis Techniques

Previously, text analysis was performed manually, which involved using keyword dictionaries and identifying recurrent terms. As a result, companies had to wait for months before getting actionable insights.

Thanks to the advancement in technologies, you can now process a massive amount of data in no time. Here are the techniques that are used in text analysis.

  • Artificial Intelligence (AI): Artificial intelligence refers to the technology that imitates human behavior concerning the intelligence processes involved in problem-solving.
  • Natural Language Processing (NLP): A part of AI, NLP enables the computer program to review and understand human languages. Text analysis uses NLP to eliminate the noise from unstructured data to help you understand customers’ opinions about your business and identify trends.
  • Machine Learning (ML): A subset of AI, ML, can automatically learn from past experiences and improve itself without any manual intervention. ML categorizes new pieces of data by analyzing how the old ones were processed.
  • Deep Learning (DL): A part of machine learning, DL can process and use data to better understand the context in the unstructured data, thereby improving the accuracy of automated analysis of the text.
  • Sentiment Analysis: It refers to the ability of a computer system to determine how customers feel about your business, products, or services (positive, neutral, or negative).

#artificial intelligence (ai) #text analysis #text classification

Murray  Beatty

Murray Beatty

1596556440

AI and Text Analysis: Best Approaches To Follow

Artificial intelligence and text analysis provide you with a deep understanding of your business’s performance and customers, empowering you to make better decisions.

From automating repetitive tasks to delivering actionable customer insights, AI helps businesses to improve revenue and user experience. Similarly, text analysis interprets extensive collections of data to uncover consumer trends and opportunities.

Text analytics refers to the method of analyzing a text to extract useful, high-quality information. Around 80-90% of the data in every organization is unstructured. Text analysis uses AI and ML technologies to generate valuable insights, which you can use to make data-driven decisions.

The tremendous amount of data generated each day provides businesses with an opportunity and a challenge.

  • The opportunity: It allows companies to get in-depth insights on your customer’s opinions about your products or services.
  • The challenge: Processing a vast amount of data and generating valuable information from them.

Text analysis helps businesses overcome this challenge and make the most of this opportunity.

#artificial intelligence (ai) #text analysis #text classification