## 4 Summary of Results and Discussion
The most important threats to its validity are listed below. 1. Only three mapping matrices were explored in detail. Perhaps more models might be proposed or retrieved from the datasets available. A comparison of the mapping accuracies obtained from a different mapping model and the same datasets would provide more insight. 2. The 10%, 20% and SD measures for threshold were chosen arbitrarily. Other thresholds might be considered for other contexts and input channels. The required precision is dependent on the context of application. Although 20% of the scale might seem a broad precision margin, in most cases, it might be sufficient, as frequently only two classes of emotions are analyzed (e.g., a stress and no-stress condition). Our previous study showed, for example, that for word annotations an intra-rater inconsistency of up to 15% of the scale was encountered in 89.1%, 74.3% and 79.4% of the ratings for valence, arousal and dominance, respectively [35]. Some inconsistency in annotations with affect is imminent due to the ambiguous nature of emotion; therefore, in this study we decided to report results for 10% as well as 20% of the scale, followed by the standard-deviation-based metric. 3. The evaluation is based only on sets retrieved from affect-annotated lexicons. It is expected that the results might be different for sets based on alternative input channels. However, training a mapping model requires a double annotation of some media (the same word/image/sound/video being assigned values in two emotion representation models). The first challenge in this research was to actually find datasets that are annotated twice, using two different emotion representation models, and that was found true only for the presented lexicons. Basing the mapping on lexicons only might limit the generalizability of the obtained solution, but currently there are no other twice-annotated datasets available. The new matrices were obtained using the 10-fold cross-validation method for regression model. Cross-validation, although well-established, is also reported as having both advantages and disadvantages. More recent reviews suggest that traditional cross-validation needs to be supplemented with other studies [36]. Therefore, in this study, we report both cross-validation, as well as cross-set evaluation results. The purpose of the paper was to propose a method and a set of metrics for the evaluation of the mapping accuracy and to propose a new mapping according to the procedure. Despite the limitations, the purpose was achieved, the new mapping was formed, and the thesis _the proposed metric set allows for evaluation of emotion model mapping accuracy_ might be accepted. Implications of the study include the following:  
1. Affective states recognition algorithms provide estimates that might be wrong or imprecise. Additionally, any mapping between the emotion representation models might enlarge uncertainty related to emotion recognition, as there is non-zero accuracy error for all mappings. Therefore, it is always worthwhile considering an emotion recognition solution that provides an estimate in the representation model and that is better fitted into your context of applications. 2. The new mapping matrices might be applied when you need emotions described with a PA or PAD model and get [PERSON]'s six basic emotions vector form the affect recognition system. The mapping might also be found useful in a late fusion of hypotheses on emotional states obtained from multiple affect recognition algorithms. In this study, two matrices were proposed (Equations (13) and (14)) and it is important to emphasize the differences between them. The first one was derived from a smaller dataset, and is therefore less accurate, but this applies to a complete list of dimensions. However, if you expect dominance and surprise not to play an important role in your application context, you might consider the latter one, as it was derived from a bigger dataset and therefore achieved higher accuracies for all precision thresholds. 3.