Sentiment analysis: An NLP technique that analyzes text to identify its sentiments, such as “positive,” “negative,” or “neutral.” Sentiment analysis is commonly used by businesses to better understand customer feedback. 

Summarization: An NLP technique that summarizes a longer text, in order to make it more manageable for time-sensitive readers. Some common texts that are summarized include reports and articles. 

Keyword extraction: An NLP technique that analyzes a text to identify the most important keywords or phrases. Keyword extraction is commonly used for search engine optimization, social media monitoring, and business intelligence purposes. 

Tokenization: The process of breaking characters, words, or subwords down into “tokens” that can be analyzed by a program. Tokenization undergirds common NLP tasks like word modeling, vocabulary building, and frequent word occurrence. 

Chatbots answering questions

Voice assistants like Siri or Alexa

Spell check in emails or texts

Automatic language translation

Sentiment analysis in social media

Text summarization in news apps

Speech-to-text apps

Email sorting by topic or importance

Content recommendations based on reading history

Customer service automation