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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' , ...

I n my previous  article, we saw how one can utilize a prebuilt model to read data from a sales receipt. In this article, we will learn to create our own ML model, train it, and then extract information from a sales receipt. Here custom model means a model which is completely tailored to meet a specific need or a use case. Steps involved To perform this end-to-end workflow, there are 4 major steps. Step 1 - Create Training Dataset For training a model we need at least 5 documents of the same type, which means if we are planning to analyze receipts, then we need at least 5 samples of the sales receipts. If we are planning to extract data from a business card, then we need to have at least 5 samples of a business card, and so on and these documents can be either text or handwritten. Step 2 - Upload Training Dataset Once the training documents are collected, we need to upload that to Azure Storage. To perform this step, one should have  Storage Account  created on ...