tldr-python 1.0.0

Python wrapper for the TLDR text summarization and analysis API.

tldr-python

Python wrapper for the TLDR text summarization and analysis API available on RapidAPI.

Installation

pip install tldr-python

Introduction - Getting Started

tldr-python is a wrapper over the TLDR text summarization and analysis API. To use this API, create an account on RapidAPI and subscribe to the API.

Once you have a RapidAPI key, initialize a TLDR instance as demonstrated below.

from tldr_python import TLDR
tldr = TLDR('<your RapidAPI key>')

Text Summarization

You can use TLDR to summarize articles on the web with the summarize function. This function accepts either a URL or the raw text of an article on the web.

We can summarize a Wikipedia article about Python using the code below.

summary = tldr.summarize('https://en.wikipedia.org/wiki/Python_(programming_language)', max_sentences=3)
print(summary.text)

The code above produces the following summary:

'[34][35][36][37][38] History[edit] Python was conceived in the late 1980s[39] by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to ABC programming language, which was inspired by SETL,[40] capable of exception handling and interfacing with the Amoeba operating system.[67] The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML.[69] Alex Martelli, a Fellow at the Python Software Foundation and Python book author, writes that "To describe something as \'clever\' is not considered a compliment in the Python culture.'

Keyword Extraction

We can also extract keywords from an article as shown below.

keywords = tldr.extract_keywords('https://en.wikipedia.org/wiki/Python_(programming_language)', n_keywords=3)
print(keywords.json)

The result of the code above is a JSON list with the top three scored keywords from the article.

[{'keyword': 'python', 'score': 402}, {'keyword': 'language', 'score': 68}, {'keyword': 'software', 'score': 47}]

Sentiment Analysis

We can also analyze the sentiment of an article with the analyze_sentiment function. This function returns a Sentiment object with sentiment and polarity attributes.

sentiment = tldr.analyze_sentiment('https://en.wikipedia.org/wiki/Python_(programming_language)')
print(sentiment.json)

The code above gives a dictionary with the sentiment and polarity of the article.

{'sentiment': 'positive', 'polarity': 0.10235096701177092}