# Similar Word Replacement Method for Improving News Comometer Analysis
## 4 Results
### Similarity Comparison Using SWR  
In the first experiment, we evaluated how the similarity between comments increases when SWR is used. The model computed the average similarity between the first user's comment and other comments using SWR as follows:  
\[Sim_{SWR}=\sum_{j=2}^{n}Sim_{SWR}\big{(}C_{1},C_{j}\big{)}/(n-1) \tag{20}\]  
The result is shown in Figure 7. We sampled \(n\) comments from the top 500 news commenters. When SWR was not applied, the average similarity score was 0.088. However, when SRW was used, the average similarity score was 0.284. When SWR was used, similarity score increased about 3.2 times compared to when we did not use SWR. It means that we are better at finding similar comments using SWR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Num** & **Post\_ID** & **Subject** & **Company** & **Num of Comments** & **Num of Users** \\ \hline
1 & 20170407085950355 & Ahn Copies [PERSON]’s Speech & Herald & 9754 & 7427 \\ \hline
2 & 20170407163335851 & Sewol-ho wasting 0.1 B\$ & Yonhap & 9266 & 8067 \\ \hline
3 & 20170402160552920 & Taegukgi Gathering in Bongha & News 1 & 7367 & 6484 \\ \hline
4 & 20170401154147438 & Moon, Park Amnesty & Newsys & 6687 & 4917 \\ \hline
5 & 20170404111730189 & Stray TK & Edaily & 5553 & 4765 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top 5 news articles in terms of number of comments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Num** & **ID** & **Name** & **Num of Comments** & **Num of News Articles** \\ \hline
1 & 9010433 & Sleeping User & 673 & 634 \\ \hline
2 & -135404000 & Happycat & 652 & 250 \\ \hline
3 & 16476611 & Kwakyongwoo & 643 & 486 \\ \hline
4 & -144133664 & Candy & 622 & 461 \\ \hline
5 & -118722416 & Moonse– & 599 & 276 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: User data for top 5 commenters. In the following example, there are two comments, \(C_{1}\) and \(C_{2}\):  
\[C_{1}:\quad\raisebox{-1.29 pt}{\includegraphics[width=14.226378 pt]{Fig1}}\quad \raisebox{-1.29 pt}{\includegraphics[width=14.226378 pt]{Fig1}}\quad\raisebox{-1.29 pt}{\includegraphics[width=14.226378 pt]{Fig1}}\quad\raisebox{-1. We trained the seq2 seq model using comments written by 400 individuals and tested it using 100 users' comments written about news stories via five-fold cross-validation. When SWR was not used, the accuracy of the seq2 seq model was 71.71%. When we used SWR-1, its accuracy increased by 0.4% to 72.11%. When we used SWR-10, the accuracy was 77.77% and increased by 6.06% compared to when SWR was not used. Therefore, we show that the accuracy to classify each user's political stance increases. When SWR was not used, the recall of the seq2 seq model was approximately 86%, as shown in Figure 9. When we used SWR-1, its recall increased by 5% to 91%. When we used SWR-10, the recall was 86%. On the contrary, when SWR was not used, the false positive rate (FPR) of the seq2 seq model was approximately 69%, as shown in Figure 10. When we used SWR-1, its FPR decreased by 3% to 66%. When we used SWR-10, the FPR was 41% and decreased by 28% compared to when SWR was not used. Therefore, we show that the FPR to classify each user's political stance decreases. Figure 8: Accuracy. Figure 10: FPR. Figure 9: Recall. An example of SWR's role in political stance classification is as follows. The following comment is labelled as 'politically left.' However, when our model did not use SWR, it was incorrectly classified as 'politically right.'