Analyzing emotion and sentiment

The code in this repository uses a variety of approaches to analyze emotion and sentiment.

The examples were created using Python and Visual Studio code

Presentation

If you would like a copy fo the slide deck associated with this GitHub, you can find it on Slide Share

Analyzing text sentiment

Language used: Python

Services used: Azure Cognitive Service Text Analytics - Sentiment API

Related tutorials and resources:

• How to sign up for the Text Analytics API
• Text analytics Python quickstart

Files:

• AnalyzeTextSentiment.py code to analyze text and return a score from 0.0 to 1.0 indicating whether the sentiment is negative (0.0) or positive (1.0)
• DetectLanguage.py code that will detect the language in a string of text

Analyzing emotion and faces

Language used: Python

Services used: Azure Face API

Related tutorials and resources:

• What is the Azure Face API
• Face API Notebook
• Analyze a local image using REST API and Python
• Analyze a remote image using REST API and Python

Files

• CheckEmotionHTTPCall.py code to locate faces in an image and return facial characteristics and a score from 0.0 to 1.0 for an assortment of emotions

Generating a word cloud with Power BI and Text Analytics

Language used: F#

Services used: Azure Cognitive Services Text Analytics - Key Phrases API

Related tutorials and resources:

• Tutorial Power BI Word Cloud with Text Analytics
• How to extract key phrases using Text Analytics
• Power BI for report designers
• BI Desktop Quickstart learn DAX basics

Files

• CirqueDeSoleilReviews.csv csv file containing reviews of the Cirque de Soleil show Love you can use to extract keyphrases and generate a word cloud
• ExtractKeyPhrases.py not required to generate the word cloud from Power BI Desktop, but this code sample shows you how to call the text analytics service to extract key phrases directly from your Python code