What types of outputs can Sentiment analysis models generate?

Sentiment analysis models are designed to analyze text and assign a sentiment score indicating how the text expresses emotional attitudes. The types of outputs these models can generate typically include positive, negative, and neutral classifications.

The positive classification indicates that the text expresses a favorable view, while a negative classification signifies an unfavorable view. The neutral classification represents text that does not convey a strong positive or negative sentiment, often describing content that is neutral in tone or objective in nature. This three-class output allows for a more granular understanding of sentiments conveyed in the data being analyzed, accommodating a wide range of expressions found in language.

While there might be other classification schemes, such as mixed sentiments, they are often extensions or more complex models. However, in basic sentiment analysis, the standard output of positive, negative, or neutral provides a clear and sufficient understanding of the sentiment within the text, making it the most fundamental classification used.