The PySpark-BigQuery and Spark-NLP codelabs each explain "Clean Up" at the end. Running through this codelab shouldn't cost you more than a few dollars, but it could be more if you decide to use more resources or if you leave them running. Next, you'll need to enable billing in the Cloud Console in order to use Google Cloud resources. Sign-in to Google Cloud Platform console ( ) and create a new project: If you don't already have a Google Account (Gmail or Google Apps), you must create one. An excellent introduction to LDA can be found here. To build our model, we'll use an algorithm called Latent Dirichlet Allocation (LDA), which is often used to cluster text. In other words, we can build a topic model on our corpus of Reddit "posts" which will generate a list of "topics" or groups of words that describe a trend. Topic modeling is a statistical method that can identify trends in the semantic meanings of a group of documents. One approach for doing this is via a NLP method known as "topic modeling". We have access to a corpus of text data in the form of posts from the Reddit subreddit r/food that we'll use to explore what people are talking about. The Chief Data Scientist of our (fictional) organization, "FoodCorp" is interested in learning more about trends in the food industry. This can certainly be a daunting task! Fortunately, we'll take advantage of libraries like Spark MLlib and spark-nlp to make this easier. ![]() ![]() We'll explore how to use NLP on large amounts of textual data at scale. It's an active area of research that's transforming the way we work with text. NLP can be used for everything from translating languages to analyzing sentiment to generating sentences from scratch and much more. As the amount of writing generated on the internet continues to grow, now more than ever, organizations are seeking to leverage their text to gain information relevant to their businesses. Natural Language Processing (NLP) is the study of deriving insight and conducting analytics on textual data.
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