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Table of contents
1.
Introduction
2.
Unstructured Data
3.
Text Analytics
4.
Keyword Search
5.
Differences 
6.
Frequently Asked Questions
6.1.
What is unstructured data?
6.2.
What are the 5V’s of Big Data?
6.3.
Why is Big Data important?
6.4.
Name some big data text analytics tools.
7.
Conclusion
Last Updated: Mar 27, 2024

Text Analytics VS Keyword Search

Author Pankhuri Goel
0 upvote
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

With each passing second, the amount of data shared and transferred between humans grows exponentially. Organising, analysing, predicting, and making decisions based on such data is daunting. Companies today strive to understand the most recent market trends, customer preferences, and other requirements, which necessitates the interpretation of massive amounts of data as the main asset.

 

Big data is a collection of structured, semistructured, and unstructured data gathered by organisations that can be mined for information and used in machine learning, predictive modelling, and other advanced analytics initiatives. Big data processing and storage systems, as well as technologies that facilitate big data analytics, have become a regular component of data management architectures in businesses.

Unstructured Data

Unstructured data, as the name implies, is data that does not have a defined structure or format. Most of the information is in the form of unstructured data. The fundamental difference between structured and unstructured data is that the data structure is volatile in the latter.

 

Unstructured data contains information maintained internally, such as files, e-mails, and customer communication, as well as external information sources, such as tweets, blogs, YouTube videos, and satellite images, that are relevant to the firm. The amount and variety of this data are continually increasing. Companies are increasingly seeking to grasp the consequences of this plethora of data for their businesses today and in the future.

 

Documents, e-mails, texts, log files, tweets, and many more are unstructured data sources. While documents and e-mails may have some kind of structure, tweets and texts might contain slang and various abbreviations that make little to no sense. On the one hand, log files have a completely different type of structure associated with them. 

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Text Analytics

The process of analysing unstructured text, extracting essential information, and translating it into structured data that may be used in a variety of ways is known as text analytics. 

Text analytics combines machine learning, statistical, and linguistic tools to deduce insights and patterns from vast amounts of unstructured text or text that does not have a specified format. 

Text analytics is being employed in a wide range of big data applications, from social media analysis to warranty and fraud analysis. Furthermore, businesses are increasingly turning to a combined perspective of structured and unstructured data to acquire a complete picture.

It lets corporations, governments, researchers, and the media make critical decisions using the vast amounts of data available. Sentiment analysis, topic modelling, named entity identification, phrase frequency, and event extraction are some of the approaches used in text analytics.

Keyword Search

When we utilise keyword (word) search, we type a few terms into a search engine, and the machine returns one or more documents that include those words. Each hit corresponds to a single document, which you must study to determine whether it is relevant. As a result, if you have 1000 hits, you must read 1000 documents.
 

It is basically the process of finding the documents and texts that contain a specified word or group of words.

Differences 

Search is the process of fetching a document based on what the end-user already knows they seek. The goal of text analytics is to find information. While text analytics is not the same as search, it can help to supplement search strategies. Text analytics paired with search, for example, can be used to improve document categorisation or classification and to generate abstracts or summaries. They both work on unstructured data.
 

The end-user can utilise a keyword search to locate papers that contain the names of a company’s competitors. A group of documents would be returned from the search. The end-user could only find relevant answers to their questions by reading the documents. It is retrieval-based.

 

Text analytics returns fragments of information that require human intervention to assemble and interpret. It gathers insight from texts.

Frequently Asked Questions

What is unstructured data?

The data that does not follow a specified format is known as unstructured data. It can be videos, images, texts, etc. Both text analysis and keyword search are done on unstructured data.

What are the 5V’s of Big Data?

The 5V’s of Big Data are Volume, Velocity, Variety, Veracity, Value. 

Why is Big Data important?

Companies use big data in their systems for improving operations, offering better customer service, generating targeted marketing campaigns, and taking other activities that can raise revenue and profitability in the long run. Businesses that properly use it have a potential competitive advantage over those that don’t because they can make better, faster judgments.

Name some big data text analytics tools.

Attensity, Clarabridge, IBM, OpenText and SAS are various big data text analytics tools.

Conclusion

In this article, we learned about the primary differences between text analytics and text search in Big Data.

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