classes. It is important in datasets with high skewness (as is the case here) or in problem in which we are more interested in the effectiveness in the majority classes.

0 The first **evaluation metric** is the popular (Micro)F1-score. It can be calculated by computing the amount of correctly predicted instances as positive and negative for an individual class in terms of precision and recall. The precision rate is the proportion of retrieved instances that was correctly classified for this class, while recall is the proportion of real instances of this class that was retrieved from all dataset. Then, the F-Score is the harmonic mean of precision $p$ and recall $r$, calculated for all classes as:

$$\label{eq:f1score}
      F_1 = 2 \cdot \frac{p \cdot r}{p+r}$$

We also use Macro-F1, or macro-average measure, to compute the F1 score among all labels. This method can be used when we want to know how the system performs overall across different classes. As we have three different classes (positive, negative and neutral), we calculate an average precision and recall per class as next equation:

$$\label{eq:macrof1}
     ( p_{macro},  r_{macro} )=  \bigg( \frac{1}{q}  \sum_{\lambda=1}^q p_\lambda , \frac{1}{q} \sum_{\lambda=1}^q r_\lambda \bigg)$$

Where $\lambda$ is a label and $L 