Sentiment Analysis

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Sentiment analysis, also called "mood recognition", is a sub-area of text mining and describes the automatic evaluation of texts with the aim of recognising an expressed attitude as positive or negative. [1]

How it works

The task of sentiment detection is approached by statistical methods. In addition, one can include the grammar of the statements examined. Statistical analysis is based on a basic set of terms (or N-grams) 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, algorithms of machine learning can be applied. On the basis of preprocessed texts, to which the postures are known, such algorithms can also learn for further concepts 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. If, for example, the grammar of the texts is analyzed, 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:

 

offerer 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