Feature Transform of Text Data — NLP

Feature Transformation is the process of converting raw data which can be of Text, Image, Graph, Time series etc… into numerical feature (Vectors). So that we can perform all algebraic operation on it.

Text data usually consists of documents which can represent words, sentences or even paragraphs of free flowing text_._ The inherent unstructured (no neatly formatted data columns!) and noisy nature of textual data makes it harder for machine learning methods to directly work on raw text data. Hence, in this article, we will explore some of the most popular and effective strategies for transforming text data into feature vectors. These features can then be used in building machine learning or deep learning models easily.


Document- A “document” is a distinct text. This generally means that each article, book, or so on.

**Corpus- **A Corpus is collection of documents.

**Vocabulary- **The set of unique words used in the text corpus is referred to as the vocabulary.

Feature Transform Technique’s

1. Bag of Words (BOW)

2. Term Frequency and Inverse document Frequency (TFIDF)

3. Word Embedding using Embedding Layer

4. Word to Vectors

Important point to note here is before performing any of the above mentioned Feature Transformation Technique its mandatory to perform text preprocessing to standardize data, remove noise and reduce dimension. Here is my blog on text preprocessing.

1. Bag of Words (BOW)

This is one of the most simple vector space representational model for unstructured text. A vector space model is simply a mathematical model to represent unstructured text (or any other data) as numeric vectors, such that each dimension of the vector is a specific feature\attribute. The bag of words model represents each text document as a numeric vector where each dimension is a specific word from the corpus and the value could be its frequency in the document, occurrence (denoted by 1 or 0) or even weighted values. The model’s name is such because each document is represented literally as a ‘bag’ of its own words, disregarding word orders, sequences and grammar.

Lets say our corpus have 3 documents as follows

I. This movie is very scary and long

II. This movie is not scary and is slow

III. This movie is spooky and good

we need to design the Vocabulary, i.e. list of all unique the words(ignoring case and punctuation).so we end up with following words ‘this’, ‘movie’, ‘is’, ‘very’, ‘scary’, ‘and’, ‘long’, ‘not’, ‘slow’, ‘spooky’, ‘good’. This words individually represents a dimension in vector space.

Because we know the vocabulary has 11 words, we can use a fixed-length of 11 to represent each document, with one position in the vector to score each word. The simplest scoring method is to mark the presence of words as a boolean value, 0 for absent, 1 for present. For example “this movie is very scary and long” can be represented as [1 1 1 1 1 1 1 0 0 0 0].Same way we can represent all the document and arrive at document term matrix as below.

#feature-extraction #embedding #word2vec #data-science

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Feature Transform of Text Data — NLP
Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

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This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Uriah  Dietrich

Uriah Dietrich


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Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

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Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data