• Search by Module
• Search by Words
• Search Projects

• Java
• Python
• JavaScript
• TypeScript
• C++
• Scala
• Blog

Project: reviewer_experience_prediction (GitHub Link)

• reviewer_experience_prediction-master
  • src
    • mongodb.pyx
    • __init__.py
    • features.pyx
    • datasets.pyx
    • experiments.pyx
  • apln_paper
    • screens
  • slides
  • reports
    • Counter_Strike_Global_Offensive_report.txt
    • Garrys_Mod_report.txt
    • Arma_3_report.txt
    • Sid_Meiers_Civilization_5_report.txt
    • Counter_Strike_report.txt
    • The_Elder_Scrolls_V_report.txt
    • Warframe_report.txt
    • Grand_Theft_Auto_V_report.txt
    • Dota_2_report.txt
    • Team_Fortress_2_report.txt
    • Football_Manager_2015_report.txt
  • LICENSE
  • util
    • get_review_data.py
    • get_review_data_all_games.sh
    • make_arff_files.py
    • create_mongodb_index.py
    • cv_learn.py
    • __init__.py
    • extract_features.py
    • make_train_test_sets.py
  • montclair_thesis
    • NLP_and_Machine_Learning_Applied_-_Video_Game_Reviews.tex
    • NLP_and_Machine_Learning_Applied_-_Video_Game_Reviews.log
    • acl.bst
    • acl2015.sty
    • NLP_and_Machine_Learning_Applied_-_Video_Game_Reviews.aux
  • setup.py
  • conda_requirements.txt
  • data
    • sample.jsonlines
    • __init__.py
  • jupyter_notebooks
    • Exploring_Label_Distribution.ipynb
    • Exploring_Review_Data.ipynb
    • Incremental_Learning_with_scikit-learn.ipynb
    • README.md
  • README.md
  • tests
    • test_src.py
    • __init__.py
    • test_experiments.py
    • test_datasets.py
  • requirements.txt
  • unittest.cfg
  • .gitignore
  • setup.sh

Reviewer Experience Prediction

Description, Original Inspiration, Slides

• Create a system for predicting the number of game-hours that have been played by a reviewer given only the review text itself.
• Idea: The online gaming platform Steam publishes hundreds of thousands of video game reviews (along with other pieces of data) by its users and it also collects and makes available huge amounts of data linked to those very same users. So, we can look at things like...
  • How many hours a reviewer played the game he/she is reviewing
  • Whether a review was marked as helpful (and how many times this was done)
  • Whether a review was marked as humorous
  • Reviewer/user stats, such as:
    • Achievements attained
    • Number of hours played in the last 2 weeks
• This project was originally the basis for a class project for a course at Montclair State University. After the conclusion of that project, I continued the research and the bulk of the updates to the code, data, and algorithms have been made since then.
• I gave a talk about this work entitled "Applying Machine Learning and NLP to Video Game Reviews" on December 17^th, 2015 at Montclair State University. The slides for this talk can be accessed either in the "slides" directory or at this link.

Current -- and More General -- Aim

• Use the system to conduct machine learning experiments of various kinds, not just in trying to predict number of game-hours played.

Dependencies

• Conda (anaconda/miniconda), which can be found here
  • For a Linux, 64-bit system, install with:

         wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
         chmod a+x Miniconda-latest-Linux-x86_64.sh
         ./Miniconda-latest-Linux-x86_64.sh -b -p conda
         rm -f Miniconda-latest-Linux-x86_64.sh

• MongoDB, which can be found here

Set-up

• Run setup.sh to create the conda environment (must have conda installed, see Dependencies section above), which will also run setup.py.
• Activate the newly-created "reviews" environment: source activate reviews
• Optional: If making changes to the Cython modules or to re-compile them, run python setup.py develop.
• Optional: Start a MondoDB server by, for example, creating a screen session and running mongod. To specify a path for the database, use the --dbpath option flag followed by the desired path. If in a screen session, press CTRL+a+d (not at the same time, but in a row) in order to "detach" from the session.
• You're all set up!

Use

• Distributed with this package is pre-collected data for just over 10 games (in the data directory), so there is no need to actually run the web scraping utility (get_review_data).
• You will probably want to first use the make_train_test_sets utility to read the data in the data sub-directory into a MongoDB collection. You must have a MongoDB server running for this to work, obviously.
• Once the data is in the database, you can run the command-line utilties. Use extract_features to get the NLP features for each review and add them to the MongoDB database). If NLP features are extracted for all of the data available, the database will currently balloon to about 250 GiB, so it is very important to try to limit the feature extraction if resources are tight. Once features are in the database, learning experiments can be conducted. Use cv_learn for running incremental cross-validation machine learning experiments (with hyperparameter grid search cross-validation as well). A number of learning algorithms can be used as well as default parameter grids so that each experiment will try every combination during the initial grid search round of learning and output the results of the model evaluations of each sub-experiment of the cross-validation experiments.
• Unit tests: Run nose2 -v tests to run the unit tests (only a few right now).