Applied Text Analysis with Python: Enabling Language Aware by Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda

By Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda

The programming panorama of ordinary language processing has replaced dramatically long ago few years. computer studying methods now require mature instruments like Python’s scikit-learn to use versions to textual content at scale. This functional advisor exhibits programmers and knowledge scientists who've an intermediate-level realizing of Python and a easy knowing of desktop studying and traditional language processing tips on how to develop into more adept in those fascinating parts of knowledge science.

This ebook offers a concise, targeted, and utilized method of textual content research with Python, and covers issues together with textual content ingestion and wrangling, simple laptop studying on textual content, type for textual content research, entity solution, and textual content visualization. utilized textual content research with Python will help you layout and enhance language-aware info products.

You’ll find out how and why computing device studying algorithms make judgements approximately language to investigate textual content; how one can ingest, wrangle, and preprocess language info; and the way the 3 fundamental textual content research libraries in Python paintings in live performance. eventually, this booklet will show you how to layout and strengthen language-aware information products.

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Extra resources for Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning

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In the following code snippet, we are creating a multi_proc_crawl function that accepts as arguments a list of URLs and a number of processes across which to distribute the work. We then create a Pool object, which can parallelize the execution of a function and distribute the input across processes. We then call map, the parallel equivalent of the Python built-in function, on our Pool object, which chops the crawl iteration into chunks and submits to the Pool as separate tasks. After the crawl tasks have been executed for all of the items in the URL list, the close method allows the worker processes to exit, and join acts as a synchronization point, reporting any exceptions that occurred among the worker processes.

To access data from the Twitter API, we were required to register our application with Twitter. This ensures that they know what you are planning to do with their data, and can monitor, and in some cases control your access to the data. Web service providers like Twitter often impose limits on the amount of data you can retrieve from their service and also how quickly you can retrieve it. If you hit those limits, they will often cut off your access for a limited amount of time, and if you disregard those limits consistently and abuse their service, they may block your access permanently.

Web service providers like Twitter often impose limits on the amount of data you can retrieve from their service and also how quickly you can retrieve it. If you hit those limits, they will often cut off your access for a limited amount of time, and if you disregard those limits consistently and abuse their service, they may block your access permanently. Below is a review of the different methods for ingesting text from the web, in order of our preference, and the types of data typically obtained from each.

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