## 2 Materials and Methods
### The Datasets
The fourth of the lexicons, NAWL (Nencki Affective Word List), was manually annotated twice by a significant number of annotators using two different representation models, forming a natural set for this study [31; 32]. The models used for annotation were: pleasure-arousal-imageability and a subset of [PERSON]'s six, namely: happiness, anger, disgust, sadness and fear. Please note, that for this set the dimensions of dominance and surprise are omitted. The imageability dimension as an additional one was not used in this study. The annotated set includes mean and standard deviations of evaluations per dimension provided for all participants and also for males and females separately. All participants' metrics are used in this study. The number of words in the lexicon is 2902. Sample words with annotations are presented in Table 4.  
As the lexicons, and even sometimes the dimensions within one lexicon, were annotated with different scales, re-scaling was performed for comparability of the results for different set and dimensions. A scale of <0.1> was chosen for all dimensions. Re-scaling followed the definitions of mean and standard deviation for addition and multiplication. The lexicons were automatically paired, which required finding a common subset of words, then the operation of the pair-wise (same word) concatenation of the two annotations was performed. As a result of this pairing procedure, the following datasets were created for this study:  
**(1) The ANEW-MEHR dataset**  
The dataset was a result of pairing ANEW and [PERSON]'s lexicons [28; 29]. The common subset of words is the same as for the latter (smaller) lexicon (151 words). Please note that, in this dataset, there are two independent annotations in the PAD scale paired and no annotation in Ekman's dimensions. The set was created purposefully for estimating residual error, that is a result of pairing two independent mappings. The metric values, calculated based on the dataset, might be \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Word**} & **Happiness** & **Anger Mean** & **Sadness** & **Fear Mean** & **Disgust** & **Valence** & **Arousal** & **Imageability** \\  & **Mean (SD)** & **(SD)** & **Mean (SD)** & **(SD)** & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** \\ \hline strong & 4.85 (1.38) & 1.96 (1.46) & 1.42 (0.90) & 2.58 (1.45) & 1.38 (0.85) & 1.81 (0.98) & 2.58 (1.17) & 5.92 (1.2) \\ lonely & 1.04 (0.20) & 2.58 (1.88) & 5.31 (1.57) & 4.19 (1.96) & 1.92 (1.44) & \(-1.7\) (1.03) & 2.48 (1.19) & 6.11 (1.01) \\ happy & 5.85 (1.29) & 1.58 (1.63) & 1.58 (1.53) & 1.62 (1.70) & 1.31 (1.19) & 2.37 (0.88) & 3.15 (0.99) & 6.41 (0.69) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample annotations of words from the NAWL (Nencki Affective Word List) lexicon. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Word** & **Emotion Intensity** & **Happiness** & **Sadness** & **Anger** & **Fear** & **Disgust** & **Surprise** \\ \hline strong & 0.44 & 0.72 & 0.0 & 0.07 & 0.07 & 0.0 & 0.0 \\ lonely & 1.0 & 0.0 & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ happy & 1.0 & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample annotations of words from the Synseketch lexicon. treated as marginal accuracies that might be obtained from the mappings based on pairing lexicons. The detailed specification of the dataset is provided in Appendix A. **(2) The SYNE-ANEW dataset**  
The second set used in this study was paired based on ANEW [29] and Synesketch [30] lexicons. Only a common subset of words was used as an evaluation set (267 words). The set uses all dimensions of the PAD model (including dominance) and uses a complete set of [PERSON]'s six basic emotions (including surprise). The detailed specification of the dataset is provided in Appendix B. **(3) The NAWL-NAWL dataset**  
As in NAWL, [31; 32], a list of words was annotated twice, the creation of the dataset was (almost) automatic--the only operation to perform was a pair-wise (same word!)