## 4 Results
### Model Testing on the Combined Dataset (Climate Change and COVID-19 Datasets Combined)
However, comparing the results obtained from the n-gram analysis in relation to the analysis using the combined technique, it follows that the combined approach achieved a higher accuracy on almost all selected sets of n-grams (while testing the models trained on DS3 on dataset DS2). The maximum accuracy achieved by analyzing bigrams and trigrams using the logistic regression is 75.42% while the Naive Bayes and random forest achieve a maximum of 73.33% by analyzing (1,3)-grams and unigrams. The highest accuracy obtained using the combined approach analysis (78.33%) was achieved with a classification using the random forest model, followed by multinomial Naive Bayes with 77.50%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**DS2 Models Tested on DS2**} & \multicolumn{4}{c}{**DS3 Models Tested on DS2**} \\ \cline{2-10}  & & **LR** & **SVM** & **NB** & **RF** & **LR** & **SVM** & **NB** & **RF** \\ \cline{2-10}  & LIWC & 74.62 & 73.32 & 74.58 & 69.98 & 72.04 & 70.38 & 71.58 & 70.33 \\ \hline \multirow{4}{*}{**1,1**} & **n-grams** & 67.69 & 70.76 & 69.62 & 66.09 & 61.67 & 61.67 & 62.92 & 73.33 \\  & **n-grams** + & **75.05** & 73.37 & 72.55 & 71.68 & 70.00 & **76.67** & 72.08 & 72.92 \\ \hline \multirow{4}{*}{**1,2**} & **n-grams** & 69.22 & 66.59 & 73.86 & 63.95 & 66.67 & 66.25 & 72.08 & 65.83 \\  & **LIWC** & 73.79 & 74.64 & **76.39** & 71.67 & 72.92 & 71.25 & 72.92 & 73.33 \\ \hline \multirow{4}{*}{**2,2**} & **n-grams** & 69.17 & 72.05 & 70.04 & 67.45 & 75.42 & 72.92 & 68.75 & 59.17 \\  & **LIWC** & 73.80 & **76.34** & 70.87 & **72.97** & 70.00 & 74.17 & 68.75 & 69.58 \\ \hline \multirow{4}{*}{**1,3**} & **n-grams** & 70.49 & 65.69 & 71.29 & 64.84 & 65.83 & 64.58 & 73.33 & 63.33 \\  & **n-grams** + & **74.64** & 74.64 & 73.82 & 71.25 & 73.33 & 73.33 & 75.00 & 69.58 \\ \hline \multirow{4}{*}{**2,3**} & **n-grams** & 71.67 & 68.71 & 70.91 & 70.43 & 66.25 & 65.83 & 66.25 & 62.08 \\  & **n-grams** + & **74.66** & 74.64 & 69.20 & 72.93 & **76.67** & 69.58 & 68.33 & **78.33** \\ \hline \multirow{4}{*}{**3,3**} & **n-grams** & 59.33 & 58.89 & 59.73 & 57.59 & 60.83 & 62.08 & 59.17 & 53.75 \\  & **LIWC** & 73.77 & 72.48 & 65.20 & 69.98 & 73.75 & 76.25 & **77.50** & 73.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of models trained and tested on DS2 (COVID-19 dataset) with the models trained on DS3 (combined dataset) and tested on DS2. Although the analysis with n-grams trained on DS3 on DS2 data during the testing of the models achieved slightly less favorable results than the analysis with the combined techniques, it also achieved slightly higher accuracy maxima than the analysis with LIWC while the average value across all selected sets of n-grams was somewhat lower. The DS3 models tested on the DS1 dataset achieved a higher maximum accuracy of predictions compared to the DS3 models tested on the same dataset by analyzing n-grams and LIWC. Figures 5 and 6 graphically show the results obtained by analyzing n-grams and n-grams with LIWC for the models trained on DS2 and DS3, respectively, and tested on DS2 data. The difference in model performance when using different data processing techniques, which was previously mentioned, is clearly shown from the figures. The accuracies obtained using the models trained on the combined DS3 data and tested individually on DS1 and DS2 datasets given in Tables 6 and 7 are extracted and compared for each model and shown in Table 8. For each column, representing the performance of a specific model on a specific dataset, the maximum value achieved using the n-gram analysis and LIWC is indicated in red, the maximum accuracy obtained with the n-gram analysis is indicated in blue, and the highest accuracy achieved with the LIWC analysis is indicated in green. Yellow horizontal lines indicate those results that exceed the average obtained using a specific model on a given dataset through all combinations of n-grams (trend by model).