# Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
## 5 Discussions  
From the experimental results, some observations about the proposed approach are shown as follows:  
* As shown in Table 2, the sentiments of short texts can be effectively classified by deep learning methods such as LSTM and CNN. LSTM slightly outperforms CNN, and they both outperform ELM and NB. * As shown in Figure 3, better performance can be achieved by training suitable word embedding models from datasets with similar topics. They provide more related contexts for words in sentiment classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sent** & **SoV** & **SoVp** & **SoVn** & **Sent-SoV** \\ \hline \multirow{2}{*}{Favors [PERSON]} & 306,125 & 170,740 & 35,477 & 339,491 & 316,450 \\  & (50.09\%) & (26.51\%) & (13.52\%) & (67.24\%) & (48.46\%) \\ \multirow{2}{*}{Favors [PERSON]} & 303,987 & 473,977 & 227,546 & 165,085 & 335,542 \\  & (49.90\%) & (73.48\%) & (86.47\%) & (32.75\%) & (51.53\%) \\ \multirow{2}{*}{Neutral} & 50,903 & 16,298 & 397,992 & 156,439 & 9,023 \\  & (7.7\%) & (2.47\%) & (60.20\%) & (23.67\%) & (1.37\%) \\ \multirow{2}{*}{MAE} & 6.96\% & 16.60\% & 29.59\% & 24.11\% & 5.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison for different ways of opinion score calculation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **No Post Inf.** & **Post Inf.** & \
\begin{tabular}{c} **Normal Post** \\ **Inf.** \\ \end{tabular} & **No User Inf.** & **User Inf.** &
\begin{tabular}{c} **Normal** \\ **User Inf.** \\ \end{tabular} \\ \hline \multirow{2}{*}{Favors} & 307,132 & 321,655 & 316,450 & 316,450 & 316,450 & 316,450 \\  & (47.57\%) & (49.28\%) & (48.46\%) & (48.53\%) & (43.80\%) & (48.46\%) \\ \multirow{2}{*}{Favors} & 337,424 & 330,337 & 335,542 & 335,542 & 335,542 \\  & (52.42\%) & (50.71\%) & (51.53\%) & (51.46\%) & (56.19\%) & (51.53\%) \\ \multirow{2}{*}{Neutral} & 16,459 & 9023 & 9023 & 9023 & 9023 & 9023 \\  & (2.49\%) & (1.37\%) & (1.37\%) & (1.37\%) & (1.37\%) & (1.37\%) \\ \multirow{2}{*}{MAE} & 4.45\% & 6.16\% & 5.34\% & 5.41\% & 0.68\% & 5.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison for different user/post influence scores. * With suitable edge weighting in user-post graph for user influence analysis, the top-ranked influential users identified by our proposed method can effectively cover authenticated users in Twitter. We can even discover high influential users that are yet to be included as authenticated users in Twitter. In this paper, we focus on the aggregate stance of the general public. It is the initial investigation on the issue of comparing the effects of various relative weights of different user actions on the social influence. By applying different combinations of relative weighting, we expect to gain some insights on their impacts. More studies are needed to further find out the relation between edge weights and real user influence. * With the combination of tweet sentiments and user participation, our proposed method can effectively extract public stance for popular topics such as presidential election from social media. The best Mean Absolute Error (MAE) of 0.68% can be achieved when we combine user influence scores when aggregating public stances on a set of comparable topics. In opinion polls, either by in-person surveys, on the phone, or online, some individuals will pretend (or masquerade) their opinions or hide their true positions or thoughts. There are even \"concern trolls\" who appear to be on one side of the discussion, while pretending to be on the other side with the intention of undermining or derailing genuine discussion. Thus, the results of opinion polls might be different from the real situation. In a publicly shared platform such as Twitter, the public will notice if there are massive behaviors of spamming or \"masquerading\". Therefore, we assume that there're only a small number of possible masquerading behaviors in the crowd.