What technique plots the distribution of sentiment in model output?

Sentiment analysis is the most appropriate technique for plotting the distribution of sentiment in model output. This technique involves determining the emotional tone behind a body of text, which enables the identification of whether the expressed sentiments are positive, negative, or neutral.

When applied to model output, sentiment analysis can quantify the emotional content of various responses generated by the model. The results can then be visualized, often revealing trends and distributions across a dataset. For example, it can show how many outputs were categorized as positive versus negative, which is invaluable for understanding the model's behavior in generating responses.

In contrast, while n-gram analysis might provide insights into the frequency of phrases or words, it does not specifically address sentiment. Word clouds visualize word frequency but don't convey sentiment polarity. Feature engineering involves selecting and crafting input features to improve model performance but also doesn't directly focus on sentiment distribution. Each of these options has its own applications, but sentiment analysis directly aligns with the objective of visualizing sentiment distribution in model output.