wea_ondara/master
1
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

wip

Browse Source
This commit is contained in:
wea_ondara
2021-03-27 19:17:29 +01:00
parent cf63c67080
commit cd2eff7bbf
1 changed files with 5 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
text/3_method.tex
View File

@@ -23,6 +23,11 @@ This thesis investigates the following criteria to determine whether the change
\end{itemize} \end{itemize}
If these criteria improve after the change is introducted, the community is affected positively. If they worsen, the community is affected negatively. If the criteria stay largely the same, then the community is unaffected. Here it is important to note that a question may receive answers and votes after the \emph{new contributor} indicator is no longer shown and therefore these are not considered as part of the data set to analyze. If these criteria improve after the change is introducted, the community is affected positively. If they worsen, the community is affected negatively. If the criteria stay largely the same, then the community is unaffected. Here it is important to note that a question may receive answers and votes after the \emph{new contributor} indicator is no longer shown and therefore these are not considered as part of the data set to analyze.
%only when new contributor insicator is shown %only when new contributor insicator is shown
%limitations
% large sudden changes (maybe include example from analysis)
% autocorrelation?
% TODO
To measure the effect on sentiment of the change this thesis utilizes the Vader\cite{hutto2014vader} sentiment analysis tool. This decision is based on the performance in analyzing and categorizing microblog-like texts, the speed of processing, and on the simplicity of use. Vader uses a lexicon of words, and rules related to grammar and syntax. This lexicon was manually created by \citeauthor{hutto2014vader} and is therefore considered a \emph{gold standard lexicon}. Each word has a sentiment value attached to it. Negative words, for instance \emph evil, have negative values; good words, for instance \emph brave, have a positive values. The range of these values is continuous, so words can have different intensities, for instance, \emph bad has a higher value than \emph evil. This feature of instensity distinction makes Vader a valance-based approach. To measure the effect on sentiment of the change this thesis utilizes the Vader\cite{hutto2014vader} sentiment analysis tool. This decision is based on the performance in analyzing and categorizing microblog-like texts, the speed of processing, and on the simplicity of use. Vader uses a lexicon of words, and rules related to grammar and syntax. This lexicon was manually created by \citeauthor{hutto2014vader} and is therefore considered a \emph{gold standard lexicon}. Each word has a sentiment value attached to it. Negative words, for instance \emph evil, have negative values; good words, for instance \emph brave, have a positive values. The range of these values is continuous, so words can have different intensities, for instance, \emph bad has a higher value than \emph evil. This feature of instensity distinction makes Vader a valance-based approach.