Social Ethics Assitant : Offering Tools with new Sorting Criteria 🔍

Group project (Team14) for 2020 Fall CS489 Computer Ethics and Social Issues

We want to act as an assistant by offering new and personalized sorting criteria in NAVER NEWS COMMENT.

🏠 Homepage

📌 Subject / Purpose

• Comments on a website can easily influence the initial opinions of people who are new to the article.
• However, in the current comment system, everyone sees the same comments ordered by the number of likes.
• If people can easily find their subjectivity without external influence, the number of victims of Internet public opinion will be reduced.
• In order for people to truly get their own thoughts, we don't just give one answer, but we just give a tool and act as an assitant to help make their own answer.

*Help people to find their own opinion*

Usage

In Python

import main as ma

# main() : main(URL, ST1, ST2, ST3, ST4, k)
# URL indicates news article of interest
# ST1 is your weight on number of replies
# ST2 is your weight on number of likes / dislikes
# ST3 is your weight on keyword based algorithm
# ST4 is your weight on similarity based algorithm
# k determines the detailed method of Like/dislike option
# in the part of like/dislike, the score calculated by (likes + k*dislikes)
title, article, result = ma.main(default_url, 1, 2, 3, 4, -1)

print(title)
print(article)
print(result)

💙 File Configuration - MAIN

main.py

A file that defines the main function that performs all the procedures sequentially.

crawling

In this folder, we implemented news data crawling part. It can bring some information like each comments, their like/dislike number, and so on.

noun_proc.py

A file that defines all functions which are needed to calculate the score in main.py. (Konlpy, TextRank, Scikit-learn)

utilfunc.py

A file that defines a function that allows to use the process of scoring comments using TextRank with other factors.

💜 File Configuration - Sub

GUI_test.py

A simple implementation of the GUI. Results obtained from the GUI created with this file are implemented so that only the top three comments aligned with the set criteria are output.

dict_check.py

It includes function to check whether the word is korean word or not. A word like "펜실베이니아" is divided into "펜실" and "베이니아" which make the search result fail. So checking process is needed.

news.py

At extraction folder, some important keywords are extracted from the main news. With these keywords, this file searches some relevant naver news articles.

index.html, middle_page.py, result_page.py

A simple implementation of web using CGI. Configure the page using multiple styles defined in the css folder.

💚 File Configuration - Additional

crawling_output

In this folder, we save tsv format files that include results of crawling.

extraction

In this folder, we implemented extracting part of meaningful keyword. "CS489_Keyword_Extraction_ver_0_1" file is using Textrank based method to do it.

for_df_output.ipynb

A python file with jupyter notebook which is almost same with main.py, but we can easily compile the code and check the result with jupyter.

text_processing

In this folder, we save all sorting outputs by all the indicators we have devised. (.tsv format, 6 articles) noun_proc_n.ipynb describes those .tsv files. It includes all indicators and its algorithm.

Author

👤 Sol Han, Gisang Lee, Minseon Hwang, Sangwoo Jung

• Github: @pine-s
• Github: @bobopack
• Github: @comafj
• Github: @SangwooJung98

🤝 Contributing

Contributions, issues and feature requests are welcome!
Feel free to check issues page.

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