Sentiment Analysis

Companies are applying sentiment analysis to turn the abundance of online information into actionable insights. Through the use of machine learning, companies can get a deeper understanding of brand reputation, product reviews, article intent, and more.

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Overview

Sentiment analysis has a broad range of applications, from brand monitoring to market research to analysis of political or legal content. By interpreting text data at scale, organizations can get actionable insights from the diverse and disparate sources of information online. However, in order to train models that can scale these efforts, these companies need people who can pick up on the subtleties of language and accurately tag text for algorithm training. That’s where CloudFactory comes in.

Using AI to Monitor Online Behavior

Business intelligence companies that apply machine learning algorithms to publicly available information on social media networks and want to conduct analyses of content trends. Many have partnered with CloudFactory to train sentiment analysis models and manage exceptions that machine learning algorithms are not able to handle.

Review and tag social media posts and articles based on various items such as language, alcohol use, bigotry, etc.
Develop processes for managing and logging edge and corner cases so that they can be accurately categorized.

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We have been through a full production cycle of data sent through CloudFactory. We’ve fed that back into our model, trained it, deployed it and seen much, much better results and model accuracy.

Chief Technology Office

Sensor-as-a-Service Company