# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 5 Conclusions and Future Work  
In this paper, we proposed a new multitask framework for emotion classification and abuse detection. We derived the emotion labels from the existing pretrained emotion analysis model and proposed a decoder component based on cross attention, which effectively utilizeds the pseudo-label information containing noise. The decoder component used TextCNN and BiLSTM for two different tasks of abuse detection and emotion classification, and we performed ablation experiments, showing that each subcomponent in the decode module was indispensable. We verified the validity of the framework on five public datasets. Results on five datasets showed that our model (MFAE) outperformed other methods. Our method does not need a lot of auxiliary task-marking data, so it has good scalability.  
In future work, we will use the pseudo-label information containing noise in a more reasonable way and expand the simple emotion dichotomy task to multiple classification. In addition, we will also continue to study how to combine with additional pretrained models.  
Y.H.: conceptualization, methodology, software, data curation, writing--original draft preparation, writing--reviewing and editing; R.S.: visualization, investigation; F.G.: software, validation; H.X.: supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data underlying this article are available in the article.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Test Sample**} & **Abuse or** & **Positive** & **Negative** \\  & & **Not** & **Probability** & **Probability** \\ \hline \multirow{4}{*}{1} & @[PERSON]\_YouAshy friends? & & & \\  & that is my BLOOD brother b**ch... & & & \\  & he in yo city!!!’m hours away. & & & \\  & ill be through there tomo... & & & & \\ \hline \multirow{4}{*}{2} & @[PERSON]\_gay The magic wand? & & & & \\  & Choice! [PERSON] chose NOT TO help us, & & & \\  & POTUS [PERSON] chose TO help us. & & & \\  & ITS THE ECONONY STUDY PIDI & & & \\  & \#MAGA \#NoDACA & & & \\  & \#howdoyoulikeusnow & & & & \\ \hline \multirow{4}{*}{3} & I’m tired of these h**s**s & & & & \\  & saying us ni**as ai not nah & Yes & 5.6\% & 94.4\% \\  & b**ch you ai not sh**t** either tf & & & & \\ \hline \multirow{4}{*}{4} & MOTHER**KING B**e**CH D**e**K & & & & \\  & CUNTBAG LARD EATING *SS SU... & & & & \\  & \(-\)i’m gonna make this my bio & & & & \\  & tell me who this is i wa... & & & & \\ \hline \multirow{4}{*}{5} & me: be kind to everyone & & & & \\  & x spread love and positivity x & & & & \\  & me after a b**ch crosses my path: & & & & \\ \cline{1-1}  & rot in hell you dumb wh**e**e** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Case study. Some test samples which contain abuse language leading to a neutral emotion (samples 1 and 2). Most abuse language tends to show strong negative emotions (samples 3 and 4). In rare cases, the emotion labeling is unreliable (sample 5).  
**Acknowledgments:** This work was supported by National Natural Science Foundation of China (NSFC), \"From Learning Outcome to Proactive Learning: Towards a Humancentered AI Based Approach to Intervention on Learning Motivation\" (No. 62077027).  
**Conflicts of Interest:** The authors certify that there is no conflict of interest in the subject matter discussed in this manuscript.