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text/3_method.tex
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% sentiment calculation via vaderlib, write whole paragraph % sentiment calculation via vaderlib, write whole paragraph
% data sets as xml from archive.org % data sets as xml from archive.org
%cleaning data
% broken entries, missing user id
% answers in html -> strip html and remove code sections, not contribution to sentiment % answers in html -> strip html and remove code sections, not contribution to sentiment
% calc sentiment for answers % calc sentiment for answers
% about the change % about the change
% https://meta.stackexchange.com/questions/314287/come-take-a-look-at-our-new-contributor-indicator?cb=1 % https://meta.stackexchange.com/questions/314287/come-take-a-look-at-our-new-contributor-indicator?cb=1
% https://meta.stackexchange.com/questions/314472/what-are-the-exact-criteria-for-the-new-contributor-indicator-to-be-shown ; change date = 2018-08-21T21:04:49.177 % https://meta.stackexchange.com/questions/314472/what-are-the-exact-criteria-for-the-new-contributor-indicator-to-be-shown ; change date = 2018-08-21T21:04:49.177
% new user indicator visible for 1 week ... % new user indicator visible for 1 week ...
% differences in avg sentiment
% look at plots and write something that fits
%interrupted time series
% ref tutorial paper
% often used in medical fields to see if changes have an effect
% used same tensors as describe in paper, show formula and how it works, 3 tensors describe tensors and what they capture
% explain why i cose this model, captures the change, more complex model would capture more but also get more complicated, these 3 tensors are enough to see the impact
% fitting every value not aggregated values, aggregated values would have different weights, weights are too far spread, contrary to paper where person years are more or less constant
% single value fitting is better, no weight issues, as weights are taken care of via more values
% if one month has more values than another then that month affects its more as more values are present
%

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text/4_datasets.tex
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\chapter{Datasets} \chapter{Datasets}
%general
% from archive.org % from archive.org
% list of datasets % list of datasets
% selected largest dataset, smaller datasets data to sparse to take concolusions, statistcal change of outliner to big, outlines would effect the outcome by too much
% larger data sets yield in more consistent results
%sections 1 per site
\section{StackOverflow.com}
\section{math.stackexchange.com}
\section{MathOverflow.com}
\section{AskUbuntu.com}
\section{ServerFault.com}
\section{SuperUser.com}
\section{electronic.stackexchange.com}
\section{stats.stackexchange.com}
\section{tex.stackexchange.com}
\section{unix.stackexchange.com}
% general information
% #user, #questions, #answers, #votes, #avg answer/question
%plots
% #users % #users
% #questions, #answers % #questions, #answers