One of the key technologies that is employed by is the use of Microsoft Azure cloud services. Specifically a common one that is used in order to set a scoring relating to sentiment from a phrase, tweet or paragraph is Microsoft Cognitive Services.


In review then of what a sentiment scoring really means, here is a blurb  from the official documentation for the text analytics site at Microsoft:

The API returns a numeric score between 0 and 1. Scores close to 1 indicate positive sentiment and scores close to 0 indicate negative sentiment. Sentiment score is generated using classification techniques. The input features of the classifier include n-grams, features generated from part-of-speech tags, and word embeddings. English, French, Spanish, and Portuguese text are supported, with 11 additional languages in preview.

In short what we have then is when a value is close to 0 for a sentiment analysis score then the phrase / tweet / post is ranked neutral. Having a balanced set of words that is like the center of a set of scales.

When you have a sentiment analysis that is closer to 1 or > then the scales tip towards the positive sentiment ranking for the phrase / tweet / post.

Finally when the set of sentiment scales tips the opposite and is closer to -1 or < this indicates a strong  negative sentiment based on the choice of words someone used in their respective phrase / tweet / post.


Further the recent release of Microsoft Azure Databricks, allows a new level of bridging multiple sources of streaming, and descriptive data elements to craft more effective and proactive; predictive and prescriptive data stories.

3 thoughts on “Machine Learning & Cognitive Services Explained

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