## 4 Results
### Model Testing on the Combined Dataset (Climate Change and COVID-19 Datasets Combined)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**LR**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**NB**} & \multicolumn{3}{c}{**RF**} \\ \cline{2-13}  & & _Accuracy_ & _Precision_ & _Recall_ & _Accuracy_ & _Precision_ & _Recall_ & _Accuracy_ & _Precision_ & _Recall_ & _Accuracy_ & _Precision_ & _Recall_ \\ \hline \multirow{4}{*}{**1,1**} & **LIWC** & 74.40 & 73.90 & 76.40 & 74.60 & 73.94 & 77.60 & 72.20 & 70.34 & 78.00 & 74.20 & 74.09 & 71.60 \\ \cline{2-13}  & **n-grams** & 70.40 & 69.57 & 72.80 & 69.00 & 68.71 & 71.20 & 72.20 & 72.72 & 71.20 & 69.00 & 67.18 & 69.20 \\ \cline{2-13}  & **n-grams** & 75.00 & 74.89 & 76.80 & 73.80 & 72.97 & 77.20 & 72.20 & 72.06 & 72.80 & 71.60 & 71.89 & 70.40 \\ \hline \multirow{4}{*}{**1,2**} & **n-grams** & 72.00 & 70.71 & 76.00 & 70.60 & 69.83 & 73.60 & 74.00 & 69.79 & 72.80 & 69.20 & 66.36 & 66.40 \\ \cline{2-13}  & **n-grams** & 75.60 & 75.20 & 77.60 & 76.60 & 74.98 & 80.40 & 73.40 & 72.74 & 75.20 & **75.40** & 71.43 & 70.00 \\ \cline{2-13}  & **n-grams** & 72.80 & 72.29 & 75.60 & 71.80 & 71.05 & 75.20 & 72.80 & 73.06 & 73.20 & 69.60 & 70.51 & 69.20 \\ \cline{2-13}  & **n-grams** & **76.20** & 75.19 & 79.20 & **77.00** & **75.52** & 80.80 & **75.20** & 74.10 & 78.40 & 74.00 & 73.48 & 75.20 \\ \hline \multirow{4}{*}{**1,3**} & **n-grams** & 71.60 & 70.70 & 74.40 & 70.40 & 70.34 & 71.60 & 73.40 & 72.39 & 76.00 & 68.60 & 70.93 & 70.40 \\ \cline{2-13}  & **n-grams** & 75.60 & 75.12 & **88.60** & 74.20 & 73.12 & 77.20 & 74.00 & 73.20 & 76.40 & 73.20 & 72.20 & 73.60 \\ \cline{2-13}  & **n-grams** & 70.20 & 69.46 & 73.60 & 70.20 & 69.09 & 74.00 & 71.40 & 70.43 & 74.40 & 67.20 & 68.75 & 72.80 \\ \cline{2-13}  & **n-grams** & 75.20 & 74.32 & 78.00 & 76.00 & 74.54 & 80.00 & 72.00 & 71.19 & 74.40 & 74.40 & 73.34 & 71.60 \\ \hline \multirow{4}{*}{**2,3**} & **n-grams** & 65.80 & 65.08 & 69.60 & 64.80 & 63.40 & 71.20 & 65.00 & 64.59 & 68.00 & 63.20 & 60.49 & 85.20 \\ \cline{2-13}  & **n-grams** & 74.20 & 73.29 & 76.80 & 73.20 & 72.83 & 74.80 & 70.40 & 69.02 & 74.80 & 75.00 & 73.86 & 73.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of selected machine learning models achieved by applying natural language processing techniques on the combined climate change and COVID-19 datasets (DS3). the highest precision (75.52%) compared to the other models. The maximum accuracy achieved using the logistic regression or random forest models is 76.20% and 75.40%, respectively. The multinomial Naive Bayes achieved a slightly less favourable accuracy of 75.20%, which is still better compared to those obtained using only the n-gram analysis (74.00%) or LIWC (72.20%). The maximum recall of 88.60% was achieved by using the logistic regression model on the dataset analyzed with (1,3)-grams and LIWC. Testing DS3 Models Individually on DS1 (Climate Change Dataset) and DS2 (COVID-19 Dataset)  
Models trained on the combined dataset DS3 were tested individually on the DS1 and DS2 datasets to gain insight into the possibility of model generalization and its applicability to different datasets than the one they originate from. Table 6 shows a comparison of the accuracies of the models trained on DS1 and DS3 and tested on DS1 using the LIWC analysis, n-grams, and a combination of these techniques on all selected groups of n-grams. The above data are shown graphically in Figures 3 and 4. The logistic regression and SVM models trained on the combined dataset (DS3) and analyzed with LIWC achieved the highest accuracy of 81.45%, which is higher than for those models trained on the DS1 dataset. The other models also achieved better results by analyzing the data with LIWC. The models trained on DS3 and tested on the DS1 using LIWC achieved the highest accuracy of 81.45%, which is higher than for those models trained on the DS1 dataset. The other models also achieved better results by analyzing the data with LIWC.