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

cleanup

Browse Source
This commit is contained in:
wea_ondara
2023-01-08 08:24:17 +01:00
parent a8c99b45f9
commit 6fdcf6760f
6 changed files with 20 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
text/3_method.tex
View File

@@ -19,14 +19,14 @@ This thesis investigates the following criteria to determine whether the change
\begin{itemize}
\item \textbf{Sentiment of answers to a question}. This symbolizes the quality of communication between different individuals. Better values indicate better communication. Through the display of the \emph{new contributor} indicator, the answerer should react less negatively towards the new user when they behave outside the community standards.
\item \textbf{Vote score of questions}. This symbolizes the feedback the community gives to a question. Voters will likely vote more positively (not voting instead of down-voting, or upvoting instead of not voting) due to the \emph{new contributor} indicator. Thereby the vote score should increase after the change.
\item \textbf{Amount of first and follow-up question}. This symbolizes the willingness of users to participate in the community. Higher amounts of first questions indicate a higher number of new participating users. Higher follow-up questions indicate that users are more willing to stay within the community and continue their active participation.
\item \textbf{Amount of first and follow-up questions}. This symbolizes the willingness of users to participate in the community. Higher amounts of first questions indicate a higher number of new participating users. Higher follow-up questions indicate that users are more willing to stay within the community and continue their active participation.
\end{itemize}
If these criteria improve after the change is introduced, 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 part of the data set to analyze.
%only when new contributor insicator is shown
\section{Vader}
To measure the effect on the 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 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 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 intensity distinction makes Vader a valance-based approach.
To measure the effect on the 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 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 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 intensity distinction makes Vader a valance-based approach.
However, just simply looking at the words in a text is not enough and therefore Vader also uses rules to determine how words are used in conjunction with other words. Some words can boost other words. For example, ``They did well.'' is less intense than ``They did extremely well.''. This works for both positive and negative sentences. Moreover, words can have different meanings depending on the context, for instance, ``Fire provides warmth.'' and ``Boss is about to fire an employee.'' This feature is called \emph{Word Sense Disambiguation}.
@@ -74,7 +74,7 @@ After preprocessing the raw data, relevant data is filtered and computed. Questi
\section{Analysis}
An interrupted time series (ITS) analysis captures trends before and after a change in a system and fits very well with the question this thesis investigates. ITS can be applied to a large variety of data if the data contains the same kind of data points before and after the change and when the change date and time are known. \citeauthor{bernal2017interrupted} published a paper on how ITS works \cite{bernal2017interrupted}. ITS performs well on medical data, for instance, when a new treatment is introduced ITS can visualize if the treatment improves a condition. For ITS no control group is required and often control groups are not feasible. ITS only works with the before and after data and a point in time when a change is introduced.
ITS relies on linear regression and tries to fit a three-segment linear function to the data. The authors also described cases where more than three segments are used but these models quickly raise the complexity of the analysis and for this thesis a three-segment linear regression is sufficient. The three segments are lines to fit the data before and after the change as well as one line to connect the other two lines at the change date. Figure \ref{itsexample} shows an example of an ITS. Each segment is captured by a tensor of the following formula $Y_t = \beta_0 + \beta_1T + \beta_2X_t + \beta_3TX_t$, where $T$ represents time as a number, for instance, the number of months since the start of data recording, $X_t$ represents 0 or 1 depending on whether the change is in effect, $\beta_0$ represents the value at $T = 0$, $\beta_1$ represents the slope before the change, $\beta_2$ represents the value when the change is introduced, and $\beta_3$ represents the slope after the change.
An ITS relies on linear regression and tries to fit a three-segment linear function to the data. The authors also described cases where more than three segments are used but these models quickly raise the complexity of the analysis and for this thesis a three-segment linear regression is sufficient. The three segments are lines to fit the data before and after the change as well as one line to connect the other two lines at the change date. Figure \ref{itsexample} shows an example of an ITS. Each segment is captured by a tensor of the following formula $Y_t = \beta_0 + \beta_1T + \beta_2X_t + \beta_3TX_t$, where $T$ represents time as a number, for instance, the number of months since the start of data recording, $X_t$ represents 0 or 1 depending on whether the change is in effect, $\beta_0$ represents the value at $T = 0$, $\beta_1$ represents the slope before the change, $\beta_2$ represents the value when the change is introduced, and $\beta_3$ represents the slope after the change.
Contrary to the basic method explained in \cite{bernal2017interrupted} where the ITS is performed on aggregated values per month, this thesis performs the ITS on single data points, as the premise that the aggregated values all have the same weight within a certain margin is not fulfilled for sentiment and vote score values. Performing the ITS with aggregated values would skew the linear regression more towards data points with less weight. Single data point fitting prevents this, as weight is taken into account with more data points. To filter out seasonal effects, the average value of all data points with the same month of all years is subtracted from the data points (i.e. subtract the average value of all Januaries from each data point in a January). This thesis uses the least-squares method for regression.