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Table of contents
1.
Introduction
2.
Natural language processing
2.1.
Lexical Analysis
2.2.
Syntactic Analysis
2.3.
Semantic Analysis
2.4.
Discourse-level Analysis
2.5.
Pragmatics Analysis
3.
Text Extraction
3.1.
Feature Extraction
3.2.
Keyword Extraction
3.3.
Named Entity Recognition
4.
Frequently Asked Questions
4.1.
What are the most challenging aspects of text analysis?
4.2.
What kind of information can text analysis give you?
4.3.
What’s the difference when it comes to text analysis and text mining?
4.4.
What do you understand by text structure?
5.
Conclusion
Last Updated: Mar 27, 2024

Analysis and Extraction Techniques

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Ashwin Goyal
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Introduction

Textual data surround us. After waking up and before reaching for our first cup of coffee, we usually navigate through enormous volumes of textual material in the form of text messages, emails, and social media updates.

In Companies also, large amounts of text data are generated. Companies use various analysis and extraction technologies to extract high-quality, relevant information from massive amounts of text data.

Here we will learn about some vital analysis and extraction techniques of text information, including natural language processing (NLP).

Natural language processing

NLP's main objective is to extract meaning from text. Natural Language Processing uses linguistic elements such as grammatical structures and parts of speech. This form of analysis usually determines who did what to whom, where, when, how, and why.

Computational linguistics resulted in NLP, allowing computers to interpret written and spoken human language forms.

Natural language processing uses different levels to extract the content of each sentence and translate it into a format that computers can interpret. The primary levels for performing natural language processing tasks are:

                                                  

Lexical Analysis

Lexical analysis is concerned with the individual words in a text. It searches for morphemes, the minor units of a word.

For example, irrationally can be broken down into ir (prefix), rational (root), and ly (suffix).

The lexical analysis identifies the relationship between these morphemes and transforms the word into its root form. 

A lexical analyzer also assigns the word's possible Part-Of-Speech (POS). A lexical analysis uses a dictionary, thesaurus, or any list of words that offer information about those words.

Syntactic Analysis

Syntax analysis guarantees that the structure of a particular piece of text is proper. It aims to parse the sentence to ensure that the grammar is correct at the sentence level. A syntax analyzer assigns POS tags based on the sentence structure given the possible POS created in the preceding stage.

For example:

Correct Syntax: Code studio is the best coding practice platform.
Incorrect Syntax: coding platform practice the is best Code studio.

Semantic Analysis

Take the following sentence: "The apple ate a banana." The line is syntactically valid, yet it is illogical because apples cannot eat. Semantic analysis is the process of looking for meaning in a statement. It also deals with putting words together to form sentences.

"White Car," for example, refers to a single thing. So, we treat it as a single sentence. Similarly, names that refer to the same category, person, object, or organisation might be grouped. The term "Coding Ninjas" refers to the same organisation, not two different organisations with the names "Coding" and "Ninjas."

Discourse-level Analysis

Discourse is concerned with the impact of a prior sentence on the current sentence. In the text, “Harry is a good coder. He spends most of the time practicing codes.” Here, discourse assigns “he” to refer to “Harry.”

Pragmatics Analysis

The fifth and final phase of NLP is pragmatic. It uses a set of rules that characterise cooperative discussions to assist you in discovering the desired impact. "Open the door," for example, is read as a request rather than an order.

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Text Extraction

Extracting specific, relevant information from unstructured text data is text extraction, sometimes known as keyword extraction. Machine learning is mainly used to scan text automatically and extract keywords and phrases from unstructured text data such as surveys, news articles, and support queries.

Companies can extract useful information from enormous blocks of text without ever reading it using text extraction.  For example, we can use it to identify a product's attributes from its description quickly.

                                                         

                                                                                        Source

Text extraction is frequently combined with text classification. Typical text extraction tasks include feature extraction, keyword extraction, and named entity recognition.

Feature Extraction

The technique of extracting essential features or qualities of an entity from text data is known as feature extraction. An example is recognising a common topic in an extensive collection of text documents. It can also analyse product descriptions and extract information like model and colour.

Keyword Extraction

Extracting essential keywords and phrases from text data is known as keyword extraction. It's excellent for summarising written documents, identifying the most commonly stated attributes in customer reviews, and determining how social media people feel about a theme.

Named Entity Recognition

The text extraction task of recognising and extracting essential information (entities) from text data is known as named entity recognition (NER), also known as entity extraction or chunking. A word or a series of words might be considered an entity, such as a company's name.

Frequently Asked Questions

What are the most challenging aspects of text analysis?

In text analysis, the issue is decoding the ambiguity of human language, whereas, in-text analytics, the challenge is recognising patterns and trends from numerical results.

 

What kind of information can text analysis give you?

Text analysis software can classify, sort, and extract data from the text on its own to find patterns, correlations, sentiments, and other helpful information.

 

What’s the difference when it comes to text analysis and text mining?

Text mining refers to extracting qualitative information from unstructured text, whereas text analytics refers to extracting quantitative information.

 

What do you understand by text structure?

The way authors organise information in the text is referred to as text structures. Recognizing a text's underlying structure can assist students in focusing their attention on essential topics and relationships, anticipating what will happen next, and monitoring their comprehension as they read.

Conclusion

This article extensively discussed natural language processing (NLP), different levels of NLP, Text Extraction Solutions, and other extraction techniques.

We hope this blog has helped you enhance your Text Analysis and Big Data knowledge. You can learn more about Big DataBig Data vs. Data Science, and Big Data Engineers. 

You can also consider our Data Analytics Course to give your career an edge over others.

We wish you Good Luck! Keep coding and keep reading Ninja!!

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