How to monitor sentiment analysis in your IoT apps

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The analysis of human data is becoming pervasive in technology solutions. Using sentiment analysis – which is a sub-discipline of text analytics – offers organizations and individuals another dimension in understanding of unstructured data sets like Twitter and social feeds. When combined with real time reporting, sentiment analysis can provide valuable contextual insight enabling more precise interpretations of unstructured data. Incorporating this kind of analysis into your apps is a step towards cognitive IoT which can help improve decision making.

Sentiment analysis is a sub-discipline of the larger field of text analytics. Discovering sentiment within text or audio has always been an important part of text analytics because it provides an extra (and very human) dimension of understanding that goes beyond topical exploration of text.[1]

See how sentiment analysis works by giving this recipe a try – it gives a cool real time twitter analysis.

You can further explore how the sentiment analysis API works by watching this short video, by Pawan Lakshmanan from IBM Watson. For more in-depth information and to find out which type of analysis is right for you, download the Sentiment Analysis and Alchemy API paper. Discover how a machine learning-based approach can provide customer insight on a massive scale.

Discover more recipes using Sentiment Analysis.

End notes

[1] The business of emotion, Kurt Williams, July 2016

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