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Sentiment analysis, also called "sentiment detection", is a subfield of text mining and refers to the automatic evaluation of texts with the aim of identifying an expressed attitude as positive or negative. [1]

How it works

The task of sentiment detection is approached by statistical methods. In addition, the grammar of the utterances under investigation can be included. For statistical analysis, a basic set of terms (or N-grams) is assumed, with which positive or negative tendencies are associated. The frequencies of positive and negative terms in the analysed text are compared and determine the presumed attitude. Based on this, machine learning algorithms can be applied. On the basis of pre-processed texts for which the attitudes are known, such algorithms can also learn for further terms which tendency they are to be assigned to. With the help of natural language processing techniques, knowledge about the natural language can be incorporated into the decision. For example, if the grammar of texts is analysed, machine-learned patterns can be applied to the structure. [1]

Supplier of Sentiment Analyses

Many cloud providers have sentiment analysis. The services are listed in the following table:

 

Provider	Sentiment Analysis
Google Cloud Platform	Natural Language API
IBM	Natural Language Understanding
Microsoft	Text Analytics
Amazon	Amazon Comprehend

Many NLP services like Google Dialogflow include a sentiment analysis. [2]

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Sources

[1] [https://de.wikipedia.org/wiki/Sentiment_Detection
[2] https://cloud.google.com/dialogflow-enterprise/docs/sentiment?hl=de