By Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda
The programming panorama of normal language processing has replaced dramatically long ago few years. computer studying techniques now require mature instruments like Python’s scikit-learn to use versions to textual content at scale. This useful consultant exhibits programmers and knowledge scientists who've an intermediate-level figuring out of Python and a simple figuring out of desktop studying and ordinary language processing the best way to turn into more adept in those intriguing parts of information science.
This publication provides a concise, concentrated, and utilized method of textual content research with Python, and covers subject matters together with textual content ingestion and wrangling, uncomplicated computer studying on textual content, class for textual content research, entity answer, and textual content visualization. utilized textual content research with Python will enable you layout and advance language-aware facts products.
You’ll find out how and why laptop studying algorithms make judgements approximately language to investigate textual content; how one can ingest, wrangle, and preprocess language information; and the way the 3 basic textual content research libraries in Python paintings in live performance. eventually, this publication will help you layout and improve language-aware information products.
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Extra info for Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning
By using regular expressions you could add new categories by simply creating a directory in your corpus, and add new documents by moving them to the correct directory. Now that we have access to the CorpusReader objects that come with NLTK, we will explore how to modify them specifically for use with the HTML content that we have been ingesting throughout the chapter so far. Reading an HTML Corpus The CategorizedPlaintextCorpusReader in the previous section is actually very useful as it implements a standard preprocessing API that exposes the following methods: paras(): a generator of paragraphs, blocks of text delimited with double newlines.
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.