## 4 Method
### Dataset Investigation
In this case, the result prediction will be a list of classes with zero, one, two, or more classes that fit an item. It is common to express different emotions in a single message, for example, surprise and confusion: <<Wow! I didn't expect this to happen. What should we do next?>>. This means that the second approach, which requires excluding texts with two or more emotion categories, would reduce model accuracy and usability. Consequently, the authors decided to follow the first approach, making a model that provides independent predictions of each emotion category and, thus, can predict more than one emotion for a text piece. A common phenomenon for emotions analysis is emotions mixing. Mixed emotions are described as an expression of two or more emotions, usually the opposite ones. As mentioned before, a significant part of texts in the dataset is labeled with two or more emotions. Texts can be labeled with close emotions. Consequently, to verify if emotion mixing is presented in the dataset, we can select items labeled with emotions related to different sentiments. This results in 4200 texts selected. For example: \"That was my first live Lions game (British and flights to DTW are expensive) I've never gone through so emotions in such a short amount of time\" (excitement, realization, surprise), \"How did they respond? You can't leave out the best part\" (curiosity, disapproval). It is also worth checking if the GoEmotions dataset contains texts labeled with opposite emotions according to their sentiments. That includes 981 elements. For instance: \"I can watch test cricket, this would be no problems. Honestly though I'm annoyed I missed it, hopefully there's a re run\" (annoyance, desire), \"If he isn't respecting your clear boundaries then you should block him and move on with your life. He's not worth your time\" (caring, disapproval). Having such data supports the authors' decision to train the model to give independent predictions of each emotion category. However, the topic of emotions mixing is not further investigated in this study. Taking into account that predictions are independent, we will remove the neutral class from the dataset, meaning that the text lacks any emotional characteristics and belongs to the neutral class if no emotions were triggered. In Figure 2, we can see the class distribution in the dataset. As we can see, the dataset is imbalanced. For example, the \"admiration\" class consists of 5122 elements, the \"approval\" class has 3687, while the \"grief\" category contains only 96. As mentioned, the neutral class is to be removed and thus can be ignored in the current analysis. Considering that we are solving a multiclass, multilabel classification problem, the authors need to encode labels accordingly. Consequently, the authors applied the strategy of \"Problem transformation, whereby a multilabel problem is transformed into one or more single-label (i.e., binary or multiclass) problems\" [18] and encoded labels using a multi-hot approach. Hence, 27 target variables were created, and each of them represents a binary value, whether the text is labeled with its emotion category or not.