Skip to content

Development of An Automatic Classification System for Game Reviews Based on Word Embedding and Vector Similarity

Notifications You must be signed in to change notification settings

lbhsos/graduation-project

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

17 Commits
 
 
 
 

Repository files navigation

For refactoring

1. Refactored locally and then tested.

2. Tested file might need to merge by hand.

3. Test the merged file on each branch locally, and merge it to the master.

graduationproject

We thought that game software needs a quick feedback to maintain the software. You can see the most interest of users, through game review category classification.

For data collection

we crawled reviews from the Google Play Store to collect data

we used Selenium

fileName: [local]-crawl.py

Package

We used gensim for word2vec and konlpy for data preprocessing

Classificate game reviews using word2vec

We categorized game reviews as payment, account, configuration, server, system, directing, character, etc.

Each category contains about nine sub-words that can represent categories.

To find the categories, we internally weighted the matrix and the TDM document

fileName: [local]-refactoringCategory.py

Satisfaction measurement using CNN

We classified as satisfaction, normal, and dissatisfaction.

fileName: [local]-textcnn.py, train.py, test.py, data.py

Server setting

We used Amazon Web Services to build the server and Flask web framework.

About

Development of An Automatic Classification System for Game Reviews Based on Word Embedding and Vector Similarity

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages