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

By Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda

ISBN-10: 1491963042

ISBN-13: 9781491963043

The programming panorama of ordinary language processing has replaced dramatically some time past few years. computer studying ways now require mature instruments like Python’s scikit-learn to use versions to textual content at scale. This sensible consultant indicates programmers and knowledge scientists who've an intermediate-level knowing of Python and a simple realizing of desktop studying and usual language processing easy methods to develop into more adept in those intriguing components of information science.

This ebook provides a concise, centred, and utilized method of textual content research with Python, and covers issues together with textual content ingestion and wrangling, simple desktop studying on textual content, class for textual content research, entity solution, and textual content visualization. utilized textual content research with Python will provide help to layout and improve language-aware information products.

You’ll find out how and why computer studying algorithms make judgements approximately language to research textual content; easy methods to ingest, wrangle, and preprocess language info; and the way the 3 basic textual content research libraries in Python paintings in live performance. finally, this booklet will allow you to layout and enhance language-aware facts products.

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Additional info for Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning

Example text

Each JSON file contains a line for each tweet published to that user’s timeline. Caution It is important to read the documentation for an API and respect any specified rate limits. 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.

In order to access our text corpora, we will plan to structure our data on disk in a meaningful way, which we will explore in the next section. Corpus Disk Structure The simplest and most common method of organizing and managing a text-based corpus is to store individual documents in a file system on disk. By organizing the corpus into sub directories, corpora can be categorized or meaningfully partitioned by meta information like dates. By maintaining each document as its own file, readers can seek quickly to different subsets of documents and processing can be parallelized, with each process taking a different subset of documents.

In the specific context of text, we want the result of our ingestion process to be paragraphs of text that are ordered and complete. However, achieving this outcome is made somewhat more or less challenging depending on how the data is stored. When we ingest text data from the internet, the most common format we will encounter are HTML webpages and websites. A website is a collection of web pages that belong to the same person or organization, contain similar content, and most importantly, usually share a common domain name.

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


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