and "Good morning" sentences in the "Greetings" class, the query "Hello, good morning" will get two matches: "Good" and "morning" in the classification with a score=2.
Text classifications allow NLP to understand human language (e.g., phrasing, intent, and colloquialisms) and respond consistently through chat, text, or voice message. Multinational Naïve Bayes is a classic example of text classification and NLP: It matches terms from the input sentences and assigns a score to each classification to determine the highest scored class associated with the input. For example, if a bot’s training set contains "How are you doing?" and "Good morning" sentences in the "Greetings" class, the query "Hello, good morning" will get two matches: "Good" and "morning" in the classification with a score=2.