## 4 Results
### Model Testing on the Combined Dataset (Climate Change and COVID-19 Datasets Combined)
Yellow vertical lines indicate accuracies higher than the average obtained using all models on a certain dataset (trend by method) for each individual set of n-grams. Predictions that exceed the average defined by the trend by models and the trend by methods are colored in red. Figure 5: Models trained and tested on DS2 (COVID-19 dataset). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**LR**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**NB**} & \multicolumn{2}{c}{**RF**} \\ \hline  & \multicolumn{2}{c}{**DS3**} & \multicolumn{2}{c}{**DS3**} & \multicolumn{2}{c}{**DS3**} & \multicolumn{2}{c}{**DS3**} & \multicolumn{2}{c}{**DS3**} & \multicolumn{2}{c}{**DS3**} & \multicolumn{2}{c}{**DS3**} & \multicolumn{2}{c}{**DS3**} \\  & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} & \multicolumn{2}{c}{**Models**} \\  & \multicolumn{2}{c}{**Tested on**} & \multicolumn{2}{c}{**Tested on**} & \multicolumn{2}{c}{**Tested on**} & \multicolumn{2}{c}{**Tested on**} & \multicolumn{2}{c}{**Tested on**} & \multicolumn{2}{c}{**Tested on**} & \multicolumn{2}{c}{**Tested on**} \\  & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} \\  & \multicolumn{2}{c}{**DS1**} & \multicolumn{2}{c}{**DS2**} & \multicolumn{2}{c}{**DS1**} & \multicolumn{2}{c}{**DS2**} & \multicolumn{2}{c}{**DS1**} & \multicolumn{2}{c}{**DS1**} & \multicolumn{2}{c}{**DS2**} & \multicolumn{2}{c}{**DS1**} & \multicolumn{2}{c}{**DS2**} \\ \cline{2-9}  & \multicolumn{2}{c}{**LIWC**} & \multicolumn{2}{c}{81.54} & 72.04 & 81.54 & 70.38 & 77.31 & 71.58 & 71.38 & 70.33 \\ \hline \multirow{3}{*}{**1,1**} & n-grams & 71.92 & 61.67 & 69.62 & 61.67 & 70.77 & 62.92 & 68.85 & 73.33 \\ \cline{2-9}  & \multicolumn{2}{c}{**n-grams +**} & \multicolumn{2}{c}{74.62} & 70 & 80 & **76.67** & 83.46 & 72.08 & 78.08 & 72.92 \\ \hline \multirow{3}{*}{**1,2**} & n-grams & 73.85 & 66.67 & 72.31 & 66.25 & 73.46 & 72.08 & 68.85 & 65.83 \\ \cline{2-9}  & \multicolumn{2}{c}{**n-grams +**} & \multicolumn{2}{c}{**81.92**} & 72.92 & 80.38 & 71.25 & 77.69 & 72.92 & **80.77** & 73.33 \\ \hline \multirow{3}{*}{**2,2**} & n-grams & 73.46 & 75.42 & 67.69 & 72.92 & 73.85 & 68.75 & 69.62 & 59.17 \\ \cline{2-9}  & \multicolumn{2}{c}{**n-grams +**} & \multicolumn{2}{c}{76.92} & 70 & 76 & 74.17 & 80.77 & 68.75 & 77.69 & 69.58 \\ \hline \multirow{3}{*}{**1,3**} & n-grams & 72.69 & 65.83 & 72.69 & 64.58 & 72.69 & 73.33 & 76.15 & 63.33 \\ \cline{2-9}  & \multicolumn{2}{c}{**n-grams +**} & \multicolumn{2}{c}{76.54} & 73.33 & **82.31** & 73.33 & 77.69 & 75 & 78.08 & 69.58 \\ \hline \multirow{3}{*}{**2,3**} & n-grams & 77.31 & 66.25 & 77.31 & 65.83 & 76.92 & 66.25 & 68.46 & 62.08 \\ \cline{2-9}  & \multicolumn{2}{c}{**n-grams +**} & \multicolumn{2}{c}{73.85} & **76.67** & 78.85 & 69.58 & 80.77 & 68.33 & 76.54 & **78.33** \\ \hline \multirow{3}{*}{**3,3**} & n-grams & 67.69 & 60.83 & 68.08 & 62.08 & 67.31 & 59.17 & 65.38 & 53.75 \\ \cline{2-9}  & \multicolumn{2}{c}{**n-grams +**} & \multicolumn{2}{c}{69.23} & 73.75 & 79.62 & 76.25 & 76.54 & **77.5** & 73.85 & 73.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy comparison of DS3 (combined dataset) models tested on DS1 (climate change dataset) and DS2 (COVID-19 dataset) using selected data processing techniques. Figure 6: Models trained on DS3 (combined dataset) and tested on DS2 (COVID-19 dataset). The SVM and Naive Bayes models achieved the highest number of above-average predictions according to the trend by models and methods (11/24) (considering only the n-grams and analysis using a combined approach).