{"push_id":1791550126,"size":1,"distinct_size":1,"ref":"refs/heads/Dansteve-patch-1","head":"f86a5cec9f40e4509738704fde6fb4f0803daa2b","before":"cbee35e7872fcc6f5ebc3df44a3453e3da89f3d3","commits":[{"sha":"f86a5cec9f40e4509738704fde6fb4f0803daa2b","author":{"name":"Dansteve","email":"6c90a4bb4ee513f55ddde9b61627c83191a868a2@gmail.com"},"message":"Natural Language Toolkit (NLTK)\n\nText-based communication has become one of the most common forms of expression. We email, text message, tweet, and update our statuses on a daily basis. As a result, unstructured text data has become extremely common, and analyzing large quantities of text data is now a key way to understand what people are thinking.\r\n\r\nTweets on Twitter help us find trending news topics in the world. Reviews on Amazon help users purchase the best-rated products. These examples of organizing and structuring knowledge represent Natural Language Processing (NLP) tasks.\r\n\r\nNLP is a field of computer science that focuses on the interaction between computers and humans. NLP techniques are used to analyze text, providing a way for computers to understand human language. A few examples of NLP applications include automatic summarization, topic segmentation, and sentiment analysis.","distinct":true,"url":"https://api.github.com/repos/Dansteve/sentian/commits/f86a5cec9f40e4509738704fde6fb4f0803daa2b"}]}
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