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Voting behavior study

Python 100.00%

cqa-analysis's Introduction

cqa-analysis

DATA

Data is too large for github to store, please download from google drive, following the link below

https://drive.google.com/open?id=0BxDbPR2yNrupTmQyX0JZX3lsNms

API description

all questions under the same tage：http://apis.guokr.com/ask/question.json?retrieve_type=by_tag&tag_name=[name]&limit=[limit]

[name] tage name
question content：http://apis.guokr.com/ask/question/[qid].json

[qid] question id
answer content：http://apis.guokr.com/ask/answer/[aid].json

[aid] answer id
all answers for one question：http://apis.guokr.com/ask/answer.json?retrieve_type=by_question&question_id=[qid]&limit=[limit]
all questiones by one user：http://apis.guokr.com/ask/question.json?retrieve_type=by_user&ukey=[ukey]&limit=[limit]

[ukey] user id
all answers by one user：http://apis.guokr.com/ask/answer.json?retrieve_type=by_user&ukey=[ukey]&limit=[limit]
all supporters for one answer: http://www.guokr.com/apis/ask/answer_supporting.json?retrieve_type=by_answer&answer_id=[aid]&limit=[limit]
URL pattern for all answers that one user support: http://www.guokr.com/ask/i/[ukey]/supporting_answers/?page=[page_number]

Setup

Operating System: Ubuntu 16.04

package grey_harvest installation guidance:

terminal commandline code:

sudo apt-get install build-essential libssl-dev libffi-dev python-dev
pip install cryptography
sudo apt-get install libxml2-dev libxslt1-dev
pip install grey_harvest

cqa-analysis's People

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cqa-analysis's Issues

multi-layer voting procedure simulation

using trained voter models to do simulation in order to study:

the robustness of voting procedure
the sensitivity of the voting procedure to different rules (i.e. let the voters know who write the answer or don't let voters know that)

data collection from guokr.com

Set up Ubuntu Server

Setup Ubuntu Server on Microsoft Azure
Document the setting up process

test different rules in the simulation

feature pool generation

answer meta level feature (i.e length, number of nouns, number of verbs, ratio of pics to paragraphs etc.)
content related features (i.e answer-question similarity, sentiment analysis, etc)

classification model training

logistic model
decision trees
LDA?
SVM?

set up the multi-layer group framework

set up database

testing CouchDB as its support of JSON
MangoDB could be an option, however, it support BSON, a JSON like file format
https://www.howtoforge.com/tutorial/install-mongodb-on-ubuntu-16.04/

set up Python framework to scrape data from guokr

voter preference modeling

using collected data to train each voter's preference to answers

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