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This article talks about an end-to-end flow, wherein an email content having specific subject line will be read, email body would be analyzed using Azure Cognitive Services (Sentiment analysis), analysis results would be saved in Azure Table Storage and finally chart would be drawn in Excel. All these steps include no coding at all. You can watch out the complete recording of this flow on my YouTube channel named Shweta Lodha.

This article explains the basics of NER (Named Entity Recognition) - PII (Personal Identifiable Information) and how it can be used to redact the sensitive/confidential information before passing it to next stage. It also includes code walk through and the Azure Text Analytics instance creation. Watch out this complete flow on my YouTube channel named Shweta Lodha.

In this article, I’m going to write about another interesting Azure-based service named  Translator , which falls under the umbrella of  Azure Cognitive Services . This service helps us to translate documents from one language to another and at the same time, it retains the formatting and the structure of the source document. So, let’s say, if any text in the source document is in italics, then the newly translated document, will also have the text in italics. Key Features of Translator Service Let’s have a look at a few of the key features, of the  Translator  service, Auto-detection of the language of the source document Translates large files Translates multiple files in a shot Preserves formatting of the source document Supports custom translations Supports custom glossaries Supported document types – pdf, csv, html/htm, doc/docx, msg, rtf, txt, etc. Implementation can be done using C#/Python as SDKs a...

Today I'll be mentioning one of the useful functions of Azure Text Analytics - Sentiment Analysis. Azure text analytics is a cloud-based offering from Microsoft and it provides Natural Language Processing over raw text.  Use Case Described In this article, I will explain how to use customer-provided product reviews to understand the market insight and how one can take a call on manufacturing the products in the future. Here is the pictorial representation of this use case.   Here are the high-level steps of how we can achieve this entire flow: Step 1 This entire process starts with the data collection part and for this, I'm using a CSV file with customer-provided reviews. Here is the gist of it: Step 2 Once data is collected, we need to import the data and for that, I'm using Jupyter Notebook inside Visual Studio Code. Here is the Python code to read and extract data from CSV file: import csv feedbacks = [ ] counter = 0 with open ( 'Feedback.csv' , ...

In this article, we will learn about how we can read or extract text from an image, irrespective of whether it is handwritten or printed. In order to read the text, two things come into the picture. The first one is  Computer Vision  and the second one is  NLP , which is short for Natural Language Processing. Computer vision helps us to read the text and then NLP is used to make sense of that identified text. In this article, I’ll mention specifically about text extraction part. How Computer Vision Performs Text Extraction To execute this text extraction task, Computer Vision provides us with two APIs: OCR API Read API OCR API,  works with many languages and is very well suited for relatively small text but if you have so much text in any image or say text-dominated image, then  Read API  is your option. OCR API  provides information in the form of Regions, Lines, and Words. The region in the given image is the area t...