By Matthew A. Russell
Millions of public Twitter streams harbor a wealth of information, and when you mine them, you could achieve a few useful insights. This brief and concise booklet deals a suite of recipes that will help you extract nuggets of Twitter info utilizing easy-to-learn Python instruments. every one recipe bargains a dialogue of ways and why the answer works, so that you can speedy adapt it to suit your specific wishes. The recipes contain thoughts to:
* Use OAuth to entry Twitter information
* Create and research graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and learn buddies and fans
* observe friendship cliques
* Summarize webpages from brief URLs
This e-book is an ideal significant other to O’Reilly's Mining the Social Web.
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Extra info for 21 Recipes for Mining Twitter
11 provides an example to get you on your way. 11 Creating a Tag Cloud from Tweet Entities Problem You want to analyze the entities from timeline data (or elsewhere) and display them in a tag cloud to quickly get the gist of what someone is talking about. Solution Harvest tweets using an approach such as the one described in Example 1-20 (using the include_entities parameter in your query to have entities extracted automatically, if it’s available for your API resource), and then build a frequency map that tabulates how many times each entity occurs.
Append(cluster) # Score each cluster. The max score for any given cluster is the score # for the sentence. words('english')][:N] scored_sentences = _score_sentences(normalized_sentences, top_n_words) # Summarization Approach 1: # Filter out non-significant sentences by using the average score plus a # fraction of the std dev as a filter. 5 * std] # Summarization Approach 2: # Another approach would be to return only the top N ranked sentences. top_n_scored = sorted(scored_sentences, key=lambda s: s)[-TOP_SENTENCES:] top_n_scored = sorted(top_n_scored, key=lambda s: s) # Decorate the post object with summaries 36 | The Recipes return dict(top_n_summary=[sentences[idx] for (idx, score) in top_n_scored], mean_scored_summary=[sentences[idx] for (idx, score) in mean_scored]) # A minimalist approach or scraping the text out of a web page.
Example 1-25 is an adaptation of Example 1-23 that illustrates one possible approach. Note that you’ll also need to easy_install redis to get the redis package so that you can easily access it from Python. 40 | The Recipes Example 1-25. py) # -*- coding: utf-8 -*import sys import functools import twitter import locale import redis from recipe__make_twitter_request import make_twitter_request # A convenience function for consistently creating keys for a # screen name, user id, or anything else you'd like.
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