## 4 Experiments and Results
### Dataset  
The ReDDIT dataset [4] and PolyHope dataset [7] were used in this paper. The former dataset was created from Reddit posts, and this task aimed to identify the expressions of regret in the texts and classify them into \"regret by action\" (Action), \"regret by inaction\" (Inaction), and, if the post does not convey any regret, \"No regret\".  
Table 2 was borrowed from [4], and indicates some samples of the ReDDIT dataset. The statistics of the dataset include a total of 3425 Reddit posts that are distributed as presented in Table 3.  
In addition to the ReDDIT dataset [4], we compared the results of the transformers with their performances reported in the PolyHope dataset [7]. The PolyHope dataset was collected from tweets to build a two-level hope speech detection task; in the first level, each post was classified as \"Hope\" or \"Not Hope\", and in the second level, hope speech detection was modeled as a multi-class classification task, in which each tweet was classified as \"Generalized Hope\", \"Realistic Hope\", \"Unrealistic Hope\" or \"Not Hope\" [7]. The PolyHope dataset's statistics are presented in Table 3.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Text** & **Regret Detection** \\ \hline Ok so I’ve been falling hard for this girl recently and decided that today is & \\ the day, do I asked her if she likes me via text. She said I’m a great guy but & \\ she’s not interested right now. Ugh, I feel like I don’t know what to do anymore & \\ \(\varsigma\) (just had to post this somewhere. & \\ \hline I’ve lost so much weight, and everyone keeps saying how great I look. The & \\ medication also makes me mildly euphoric. I know it’s wrong, but I tried going & No Regret \\ off the meds before, and I went through a withdrawal and started gaining weight. & \\ \hline \multicolumn{3}{l}{\({}^{*}\)I did not really understand that credit cards are fine as long as you pay it all} & \\ off on time. So I never applied for one back then because I heard that they are & \\ bad for young people. Big mistake. I am trying to decrease the regret/self hate & \\ for this oversight” & \\ \hline I’ve made some shitty mistakes, like some really shitty ones. Sometimes I & \\ think I’m a terrible person, but that would indicate that guilt makes me not a & \\ terrible person. But thinking that excuses my problems is something that my & \\ mind can’t comprehend. I really just struggle with my thoughts sometimes. & \\ \hline I cheated on ten geometry lessons and two tests in last year. I feel awful and & \\ never want to cheat on anything again, ever. That feels better. & \\ \hline \multicolumn{3}{l}{\({}^{*}\)Yep that’s pretty much it. If they’re jewish/asian mix it’s all over. I will want to} & No Regret \\ \multicolumn{3}{l}{murry her on the spot.} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample texts from ReDDIT dataset.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Task** & **Classes** & **Count** \\ \hline Regret & No Regret & 1570 \\ Detection & Regret by Action & 1503 \\  & Regret by Inaction & 352 \\ \hline \hline \multicolumn{3}{l}{\({}^{*}\)Hope Speech} & Not Hope & 4081 \\  & Generalized Hope & 2335 \\ Detection & Realistic Hope & 982 \\  & Unrealistic Hope & 858 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of ReDDIT and PolyHope datasets.  
In this study, we used averaged weighted and macro scores to evaluate the performance of our learning models. Since Reddit posts consist of a title and main text, we trained each model using only the main text, and then again using both the text and title, in order to compare the title's contribution to the classification task.