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Monday, June 18, 2018

Sentiment Analysis Uses and Comparisons

A look at various sentiment applications and services.    Have used a couple of these.

Machine Learning as a Service: Part 1  from Towards Data Science. By Sebastian Kwiatkowski

Sentiment analysis: 10 applications and 4 services
What is sentiment analysis?

The explosive growth in user-generated content and the digitization of archive material have created massive data sets containing opinions expressed by large numbers of people on just about every single topic.

In some cases, the generation of this data is structured through the user interface. It is, for example, relatively easy to process customer reviews on e-commerce sites, because users are required to post a rating alongside the text of the product review.

Most data, however, is available in an unstructured form. It does not contain a standardized summary saying “This content expresses a positive, negative, mixed or neutral view.”

WordPress.com, for example, reports that bloggers using their platform have published more than 87 million posts just in May of 2018.[1] According to YouTube CEO Susan Wojcicki, more than 400 hours of content are uploaded to the video-sharing site every minute.[2] Meanwhile, the Google Books project has digitized at least 25 million volumes in 400 languages.[3]

Whenever a user types into a free text field or speaks into a microphone, an inference is required to categorize the sentiment.

Sentiment analysis is the field that focuses on exactly this task. It is a branch of natural language processing that studies functions designed to map a text document to a representation of a sentiment.

With the advent of accurate speech and text recognition, the reach of sentiment analysis extends beyond readily accessible digital text data and covers an increasing number of media. ... "

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