Note: this project is in no way an official or endorsed Reddit tool.
Reddit user Stuck_In_The_Matrix has created a very large archive of public Reddit comments and put them up for downloading, see: Thread on Reddit
This repository contains some tools to handle the over 900 GByte of JSON data.
Future plans are to create a simple web interface for complex queries (for example: >2000 upvotes, must not be in /r/funny or /r/pics, must contain all of (op, surely, deliver), must not contain more than one mention of (safe, picture, money) and can be from 2012 or 2013). Currently you will have to write such queries in Java (see: Search class to get an idea of how to start).
To index one month (or more...) of reddit comments and do a simple query.
- Clone or download the repository to your system.
- Download one reddit comment archive from PushShift.io (or the torrent from somewhere else.)
- You need Java 8 and Apache Maven 3 installed.
- run 'mvn clean package' to generate the archive in the 'target' folder.
- continue with 'from archive' step
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Download the distribution from cinnamon.dewarim.com.
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Unzip reddit-1.0-SNAPSHOT-distribution.zip to a folder of your choice.
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Open a command line in 'reddit-1.0-SNAPSHOT' folder.
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to create a Lucene index, where the data folder contains "2007/RC_2007-01.bz2"
java -cp lib/.:reddit-1.0-SNAPSHOT.jar com.dewarim.reddit.Main --baseDir ./data --luceneDir ./data
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to search the index:
java -cp lib/.:reddit-1.0-SNAPSHOT.jar com.dewarim.reddit.Search --luceneDir ./data/index-all -q reddit -q gold
Java classes to
- read/convert the original bz2-compressed JSON data
- create a Lucene index (skipping deleted comments, needs 24 hours single-threaded)
- add the comments to a Postgresql database (useful if you want to play with a month's worth of data)
- convert the JSON data into CSV (cuts required size in half and is easier to digest for many programs, but still has problems with line breaks in comments - wip)
Classes using Apache Spark to:
- combine sentiment CSV files and bz2-compressed JSON data to parquet format for better Spark/Hadoop querying
- count upvotes in JSON files
- find the most positive users
Python classes for (simple) sentiment analysis which can
- add sentiment data to comments existing in a PostgreSQL db (a database filled by the Java code)
- read original bz2 compressed JSON, and output sentiment data to CSV files
Just the raw data files as downloaded from PushShift.io. Please donate some bandwidth and keep them seeded for a time.
Note: My torrent server is currently offline, as I have to re-evaluate copyright and upcomming changes in EU data protection laws.
- 2005 (just 2005-12, 116 KB)
- 2006 (45 MB)
- 2007 (212 MB)
- 2008 (618 MB)
- 2009 (1.72 GB)
- 2010 (4.4 GB)
- 2011 (11 GB)
- 2012 (24 GB)
- 2013 (38 GB)
- 2014 (53 GB)
- 2015 (68 GB)
- 2016 (81 GB)
- 2017 (up to 2017-03, 23 GB)
The complete set is currently also available at academictorrents.com
// TODO (conversion finished, have to clean up dataset somewhat to rarely needed columns - and need more space on server ;)
// TODO
You can download the sentiment data via a static link (no torrent) as sentiment.tar.bz2 (14 GByte, up to 2017-02)
Current format is tab separated csv, one entry per line consisting of (roundedFinalScore, maxPosScore, maxNegScore, reddit comment id) as generated by scoreCommentsJson.py.
Data was created by using the Vader dataset from NLTK.org.
md5sum: c0964e3524555f2493a5bc29ec4c643e
sha256sum: 13f157e607bbb9b10523bf36d35527ea1953cbd027fabc88c7344a45bd3450f1
- 2018-04-29: Add command line parser by Cédric Beust (see: http://jcommander.org/) Refactored indexer and search for better command line experience. Add quick start section above.
- 2017-07-11: Add sentiment data download link
- 2017-04-15: Add torrents
- 2017-04-10: Code to extract separate date and time info.
- 2016-10-22: FindMostPositiveUsers.scala: find the 100 most positive users using the sentiment files.
- 2016-09-25: Add bash script to loop over all comment archives.
- 2016-09-22: scoreCommentsJson.py: read comments in JSON format from bz2 files with Python.
- 2016-09-22: Added sentiment analysis with Python and NLTK. For example results, see code.dewarim.com
- 2016-09-19: Upgrade to Spark 2.0
@JsonPropertyOrder(value = {"author", "name", "body", "author_flair_text", "gilded", "score_hidden", "score", "link_id",
"retrieved_on", "author_flair_css_class", "subreddit", "edited", "ups", "downs", "controversiality",
"created_utc", "parent_id", "archived", "subreddit_id", "id", "distinguished"})
doc.add(new StringField("author", comment.author, Field.Store.YES));
doc.add(new StringField("name", comment.name, Field.Store.YES));
doc.add(new TextField("body", comment.body, Field.Store.YES));
doc.add(new IntField("gilded", comment.gilded, Field.Store.YES));
doc.add(new IntField("score", comment.score, Field.Store.YES));
doc.add(new IntField("ups", comment.ups, Field.Store.YES));
doc.add(new IntField("downs", comment.downs, Field.Store.YES));
doc.add(new LongField("created_utc", comment.created_utc, Field.Store.YES));
doc.add(new StringField("parent_id", comment.parent_id, Field.Store.YES));
doc.add(new StringField("subreddit", comment.subreddit, Field.Store.YES));
doc.add(new StringField("id", comment.id, Field.Store.YES));
doc.add(new StringField("url", Comment.createLink(comment.subreddit, comment.link_id, comment.id), Field.Store.YES));
STORE.YES means the field is contained in the index and can be shown on a search result page.
See: release.md
One older example: redditors and what they expess about car brands @ code.dewarim.com
My code is free to use under the Apache License, version 2. Contributions will be accepted under the same terms and are welcome.
The simplest thing that will work.
Ingo Wiarda / ingo_wiarda@dewarim.de /u/Dewarim