# VaderSentiment **`class VaderSentiment(config)`** Bases: [`Classifier`](squirro.lib.nlp.steps.classifiers.Classifier.md#squirro.lib.nlp.steps.classifiers.Classifier) The Vader Sentiment [`Classifier`](squirro.lib.nlp.steps.classifiers.Classifier.md#squirro.lib.nlp.steps.classifiers.Classifier) applies [rule based sentiment analysis](https://github.com/cjhutto/vaderSentiment) (positive, neutral, negative) on a string. Vader sentiment provides among others a compound score to detect sentiment, which we take for the classification. **Note - Following strategy has been applied:** - positive: compound score>=0.05 - neutral: compound score between -0.05 and 0.05 - negative: compound score<=-0.05 **Input** - all input fields need to be of type [`str`](https://docs.python.org/3.11/library/stdtypes.html#str). **Output** - the output field is filled with data of type [`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict) { [`str`](https://docs.python.org/3.11/library/stdtypes.html#str), [`float`](https://docs.python.org/3.11/library/functions.html#float) }. The key of the [`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict) is the predicted class name and the value is the sentiment score. Parameters `type` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – vadersentiment **Example** ```json { "step":"classifier", "type":"vadersentiment", "input_fields":["body"], "output_field":"prediction", "label_field": "" } ``` Methods SummaryMethods Documentation **`process_doc(doc)`** Process a document Parameters `doc` ([`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)) – Document Returns Processed document Return type [Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)