Tools to work with the big reddit JSON data dump.
Java Scala Python Shell
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README.md

dewarim's Reddit-Data-Tools

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).

Quickstart

To index one month (or more...) of reddit comments and do a simple query.

From source

  • 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

From archive

  • Download the distribution from cinnamon.dewarim.com.

  • Unzip reddit-1.0-SNAPSHOT-distribution.zip to a folder of your choice.

  • Open a command line in 'reddit-1.0-SNAPSHOT' folder.

  • 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

  • to search the index:

    java -cp lib/.:reddit-1.0-SNAPSHOT.jar com.dewarim.reddit.Search --luceneDir ./data/index-all -q reddit -q gold

What's inside this repository

Java

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)

Scala

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

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

List of torrents

Raw data

Just the raw data files as downloaded from PushShift.io. Please donate some bandwidth and keep them seeded for a time.

Comments

Note: My torrent server is currently offline, as I have to re-evaluate copyright and upcomming changes in EU data protection laws.

The complete set is currently also available at academictorrents.com

Submissions

Data in Parquet format

// TODO (conversion finished, have to clean up dataset somewhat to rarely needed columns - and need more space on server ;)

Time and date

// TODO

Sentiment data

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

Changes

  • 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

Field order of CSV:

@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"})

Fields indexed with Lucene

        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.

Converting data

See: release.md

Example of working with sentiment data

One older example: redditors and what they expess about car brands @ code.dewarim.com

License

My code is free to use under the Apache License, version 2. Contributions will be accepted under the same terms and are welcome.

Code style

The simplest thing that will work.

Author

Ingo Wiarda / ingo_wiarda@dewarim.de /u/Dewarim