2.2.3 - Avoid some mention edge cases
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# twitter\_ebooks 2.2.2
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# twitter\_ebooks 2.2.3
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Rewrite of my twitter\_ebooks code. While the original was solely a tweeting Markov generator, this framework helps you build any kind of interactive twitterbot which responds to mentions/DMs.
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@ -63,7 +63,7 @@ The underlying [tweetstream](https://github.com/tweetstream/tweetstream) and [tw
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twitter\_ebooks comes with a syncing tool to download and then incrementally update a local json archive of a user's tweets.
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``` zsh
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➜ ebooks archive 0xabad1dea corpus/0xabad1dea.json
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➜ ebooks archive 0xabad1dea corpus/0xabad1dea.json
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Currently 20209 tweets for 0xabad1dea
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Received 67 new tweets
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```
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@ -75,7 +75,7 @@ The first time you'll run this, it'll ask for auth details to connect with. Due
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In order to use the included text modeling, you'll first need to preprocess your archive into a more efficient form:
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``` zsh
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➜ ebooks consume corpus/0xabad1dea.json
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➜ ebooks consume corpus/0xabad1dea.json
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Reading json corpus from corpus/0xabad1dea.json
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Removing commented lines and sorting mentions
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Segmenting text into sentences
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@ -84,7 +84,7 @@ Ranking keywords
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Corpus consumed to model/0xabad1dea.model
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```
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Notably, this works with both json tweet archives and plaintext files (based on file extension), so you can make a model out of any kind of text.
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Notably, this works with both json tweet archives and plaintext files (based on file extension), so you can make a model out of any kind of text.
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Text files use newlines and full stops to seperate statements.
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@ -103,7 +103,8 @@ module Ebooks
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mless = ev[:text]
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begin
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ev.attrs[:entities][:user_mentions].reverse.each do |entity|
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mless = mless[0...entity[:indices][0]] + mless[entity[:indices][1]..-1].strip
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last = mless[entity[:indices][1]..-1]||''
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mless = mless[0...entity[:indices][0]] + last.strip
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end
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rescue Exception
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p ev.attrs[:entities][:user_mentions]
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@ -1,3 +1,3 @@
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module Ebooks
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VERSION = "2.2.2"
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VERSION = "2.2.3"
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end
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