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Table of contents
1.
Introduction
2.
Analysis and Extraction Techniques
3.
Extracted Information
4.
Taxonomy
5.
Frequently Asked Questions
5.1.
What is big data?
5.2.
What does big data text analytics mean?
5.3.
Name some big data text analytics tools.
6.
Conclusion
Last Updated: Mar 27, 2024

Taxonomy and Big Data

Author Pankhuri Goel
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Ashwin Goyal
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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 consists of a large amount of data that cannot be processed by traditional data storage or processing devices. Many multinational organisations use it to process data and conduct business. The data flow would have been 150 exabytes per day before replication.

Analysis and Extraction Techniques

In general, text analytics systems extract information from unstructured data using a combination of statistical and Natural Language Processing (NLP) techniques. NLP is a large and sophisticated field that has grown in popularity over the last two decades. NLP's primary purpose is to extract meaning from text. Linguistic notions such as grammatical structures and components of speech are commonly used in Natural Language Processing. The goal of this type of analysis is usually to figure out who did what to whom, when, where, how, and why.

 

NLP carries out text analysis at various levels:

  • Lexical/morphological analysis looks at the features of a single word, such as prefixes, suffixes, roots, and parts of speech (noun, verb, adjective, etc.), in order to figure out what the word means in the context of the given text.
  • Syntactic analysis dissects the text and places individual words in context using grammatical structure.
  • A sentence's possible interpretations are determined by semantic analysis.
  • The discourse-level analysis aims to determine the meaning of text past the sentence level.

 

Organisations often need to design rules to extract information from diverse document sources. Organisations can create rules either manually or automatically, or a combination of both:

  • In the manual approach, someone creates a set of extraction criteria using a proprietary language. While the manual method is time-consuming, it can yield highly accurate results.
  • Machine learning or other statistical techniques may be used in automated processes. Based on a collection of training and text data, the software develops rules. To develop — that is, learn — the rules, the system first processes a series of similar documents (for example, newspaper articles). The user then runs a test data set to see if the rules are accurate.
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Extracted Information

To automate the tagging and markup of text documents, these techniques are usually supplemented with other statistical or linguistic techniques to extract the following types of information:

  • Terms are keywords.
  • Entities are specific examples of abstractions that are often referred to as named entities (tangible or intangible)
  • Facts, sometimes known as relationships, describe the who, what, and where relationships that exist between two things.
  • While some experts conflate the phrases fact, relationship, and event, others distinguish between the two, claiming that events usually have a time dimension and frequently cause facts to alter.
  • Concepts are a collection of words and phrases that suggest a specific idea or topic that the user is interested in.
  • Sentiment analysis is a technique for identifying perspectives or emotions in the underlying text.

Taxonomy

Taxonomies are frequently used in text analytics. A taxonomy is a system for categorising and organising data into hierarchical groups. It's sometimes referred to as a method of classifying things. A taxonomy makes it easier to identify and evaluate text because it explains the relationships between the various terminology a firm uses.

 

A telecommunications service provider, for example, provides both wired and wireless services. The corporation may offer cellular phones and Internet access as part of its wireless service. The corporation may then categorise cellular phone service in two or more ways, such as plans and phone types. The taxonomy could extend all the way down to the individual components of a phone.

 

Synonyms and alternate formulations can be used in taxonomies, recognising that a cellphone, cellular, and mobile phones are all the same. These taxonomies can be rather complex, and developing them can take long.

 

Some manufacturers will claim that taxonomy isn't required to use their product and that business users can categorise data that has already been extracted. This is going to be determined by the areas you're interested in. Frequently, the themes are complicated, subtle, or industry-specific. That will necessitate a well-defined taxonomy.

Frequently Asked Questions

What is big data?

Big data refers to unprocessed data that is huge and complex. This data is complex and time-consuming to process while using typical processing approaches.
 

What does big data text analytics mean?

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. Techniques from computational linguistics, statistics, and other computer science areas are used in the analysis and extraction operations.

 

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 taxonomies in Big Data. We looked into some analysis and extraction techniques. We also gained knowledge about the extracted information found after implementing these techniques.

 

We hope this blog has helped you enhance your knowledge. If you want to learn more, check out our articles on Big Data: Types, Characteristics & 5Vs – Coding Ninjas BlogBig Data: A guide for beginners – Coding Ninjas BlogBig Data Engineer Salary in Various Locations- Coding Ninjas Blog and Top 100 SQL Problems. Do upvote our blog to help other ninjas grow.

 

Head over to our practice platform Coding Ninjas Studio to practice top problems, take various guided paths, attempt mock tests, read interview experiences, solve problems, participate in contests and much more!

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