By Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda
The programming panorama of usual language processing has replaced dramatically long ago few years. computing device studying techniques now require mature instruments like Python’s scikit-learn to use types to textual content at scale. This useful advisor indicates programmers and information scientists who've an intermediate-level knowing of Python and a easy realizing of computer studying and ordinary language processing the best way to turn into more adept in those interesting parts of knowledge science.
This publication provides a concise, centred, and utilized method of textual content research with Python, and covers subject matters together with textual content ingestion and wrangling, uncomplicated computing device studying on textual content, category for textual content research, entity solution, and textual content visualization. utilized textual content research with Python will help you layout and boost language-aware information products.
You’ll learn the way and why laptop studying algorithms make judgements approximately language to research textual content; find out how to ingest, wrangle, and preprocess language info; and the way the 3 fundamental textual content research libraries in Python paintings in live performance. eventually, this ebook will assist you layout and advance language-aware facts products.
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Extra info for Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning
In the final section of this chapter we will discuss how to monitor corpora as our ingestion routines continue and the data change and grow. Whether documents are routinely ingested or part of a fixed collection, some thought must go into how manage the data and prepare it for analytical processing and model computation. The first assumption we should make is that the corpora we will be dealing with will be non-trivial — that is they will contain thousands or tens of thousands of documents comprising gigabytes of data.
To illustrate how we can work with an API to acquire some data, let’s take a look at an example. The following example uses the popular tweepy library to connect to Twitter’s API and then, given a list of user names, retrieves the last 100 tweets from each user and saves each tweet to disk as an individual document. In order to do this, you must obtain credentials for accessing the API, which can be done by following the steps below. com and sign in with your Twitter account. Once you’ve signed in, click on the Create New App button.
Once you’ve signed in, click on the Create New App button. Fill out the required fields on the form (Name, Description, and Website) and check the checkbox indicating that you’ve read their Developer Agreement. Click the Create your Twitter application button. On the next page, click on the Keys and Access Tokens tab, and copy your API Key and API Secret tokens somewhere safe. Scroll to the bottom of the page and click the Create my access token button. Under the Your Access Token section, you should now see your Access Token and Access Token Secret.
Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning by Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda