Which technology uses AI/ML to analyze text and determine its emotional tone?

Sentiment analysis is the technology that specifically leverages artificial intelligence (AI) and machine learning (ML) to examine textual data and ascertain the emotional tone behind it. This process involves categorizing text as positive, negative, or neutral, based on the sentiment expressed.

In sentiment analysis, algorithms are trained on linguistic data, allowing them to interpret nuances such as sarcasm, urgency, and context. By processing large volumes of textual data from sources such as social media, customer reviews, or surveys, sentiment analysis can help organizations understand public opinion, customer satisfaction, and overall sentiment surrounding products, services, or topics of interest.

While text mining could involve extracting useful information and patterns from text data, it does not inherently focus on the emotional tone. Data analytics encompasses a broader range of techniques for analyzing various types of data, but it does not specifically deal with emotional context. Natural Language Processing (NLP) is a fundamental technology that can include sentiment analysis as a subtask, but it is not limited to analyzing emotions. It encompasses a wider array of text processing capabilities, such as language translation, speech recognition, and more.

Thus, sentiment analysis stands out as the specific technology dedicated to determining emotional tone through AI and ML.