Which process uses machine learning to identify and categorize opinions in text as positive, negative, or neutral?

The process that specifically uses machine learning to identify and categorize opinions in text as positive, negative, or neutral is sentiment analysis. This technique involves the application of natural language processing (NLP) and machine learning algorithms to interpret and classify emotional tone conveyed in written communication. By analyzing the words and phrases used in text, sentiment analysis enables the categorization of sentiments, providing insights into public opinion, customer feedback, and overall emotional inclination regarding a subject.

Sentiment analysis extends beyond just determining the overall sentiment; it can also provide nuanced understanding by estimating the intensity of the sentiment. For instance, it can differentiate between phrases that express mild annoyance versus strong anger, thus giving deeper insights into sentiment nuances.

In contrast, the other processes mentioned have different focuses. Text classification refers to categorizing documents into predefined groups based on their content, which may or may not relate to sentiment. Emotion detection aims to identify specific emotions expressed in text, such as happiness or sadness, rather than categorizing them into broader positive, negative, or neutral sentiments. Content analysis is a broader methodological approach used to analyze the actual content of texts, including themes and biases, but does not specifically focus on sentiment categorization like sentiment analysis does.