## 4 Results
### Comparing Models  
#### 4.4.1 Comparing Model Performance across Datasets  
In Table 8, the accuracies of the selected machine learning models obtained from testing the three collected datasets DS1, DS2, and DS3 using LIWC, n-grams, and the combining n-gram analysis with LIWC. For each model and each dataset in the table, the maximum achieved with the n-gram analysis is marked in blue while the maximum obtained by the combined technique analysis is marked in red. The maximum accuracy given by the LIWC analysis is marked in green. For each model, the average accuracy obtained by analysis with LIWC, n-grams and the combined approach was calculated for each of the given datasets (trend by model). In Table 9, values that exceed the average accuracy for a specific model (trend by models) or for a specific data processing method (trend by methods) are colored in yellow while values that exceed both trends are colored in red. \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}  & & **DS1** & **DS2** & **DS3** & **DS1** & **DS2** & **DS3** & **DS1** & **DS2** & **DS3** & **DS1** & **DS2** & **DS3** \\ \cline{2-13}  & **LIWC** & 78.02 & 74.62 & 74.40 & 77.24 & 73.32 & 74.60 & 76.47 & 74.58 & 72.20 & **78.05** & 69.98 & 74.20 \\ \hline \multirow{4}{*}{**1,1**} & n-grams & 75.00 & 67.69 & 70.40 & 72.74 & 70.76 & 69.00 & 76.15 & 69.62 & 72.20 & 72.71 & 66.09 & 69.00 \\ \cline{2-13}  & **n-grams + LIWC** & 78.02 & **75.05** & 75.00 & 76.14 & 73.37 & 73.80 & 76.91 & 72.55 & 72.20 & 73.43 & 71.68 & 71.60 \\ \hline \multirow{4}{*}{**1,2**} & n-grams & 79.16 & 69.22 & 72.00 & 78.80 & 66.59 & 70.60 & 81.47 & 73.86 & 74.00 & 74.62 & 63.95 & 69.20 \\ \cline{2-13}  & **n-grams + LIWC** & 78.80 & 73.79 & 75.60 & 75.77 & 74.64 & 76.60 & **81.47** & **76.39** & 73.40 & 74.96 & 71.67 & **75.40** \\ \hline \multirow{4}{*}{**2,2**} & n-grams & 78.79 & 69.17 & 72.80 & 76.52 & 72.05 & 71.80 & 79.56 & 70.04 & 72.80 & 72.38 & 67.45 & 69.60 \\ \cline{2-13}  & **n-grams + LIWC** & 78.40 & 73.80 & **76.20** & 77.21 & **76.34** & **77.00** & 77.26 & 70.87 & **75.20** & 74.22 & **72.97** & 74.00 \\ \hline \multirow{4}{*}{**1,3**} & n-grams & 80.68 & 70.49 & 71.60 & 76.13 & 65.69 & 70.40 & 79.94 & 71.29 & 73.40 & 75.04 & 64.84 & 68.60 \\ \cline{2-13}  & **n-grams + LIWC** & 79.56 & 74.64 & 75.60 & 76.52 & 74.64 & 74.20 & 78.77 & 73.82 & 74.00 & 75.34 & 71.25 & 73.20 \\ \hline \multirow{4}{*}{**2,3**} & n-grams & 76.14 & 71.67 & 70.20 & 76.14 & 68.71 & 70.20 & 78.42 & 70.91 & 71.40 & 71.25 & 70.43 & 67.20 \\ \cline{2-13}  & **n-grams + LIWC** & 79.16 & 74.66 & 75.20 & **79.52** & 74.64 & 76.00 & 79.57 & 69.20 & 72.00 & 74.97 & 72.93 & 74.40 \\ \hline \multirow{4}{*}{**3,3**} & n-grams & 68.99 & 59.33 & 65.80 & 68.59 & 58.89 & 64.80 & 70.11 & 59.73 & 65.00 & 67.08 & 57.59 & 63.20 \\ \cline{2-13}  & **n-grams + LIWC** & 78.80 & 73.77 & 74.20 & 74.96 & 72.48 & 73.20 & 74.92 & 65.20 & 70.40 & 74.20 & 69.98 & 75.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of model accuracies on DS1 (climate change dataset), DS2 (COVID-19 dataset), and DS3 (combined dataset) using the selected data processing techniques. Statistically, multinomial Naive Bayes (22/36) achieved the most above-average results per model with the n-gram analysis and with the combination of n-gram analysis and LIWC while the logistic regression competes with 21/36 above-average predictions. By classifying using the random forest model, 20/36 above-average values were obtained while the SVM achieved 19/36. Analyzing the results with n-grams and a combined approach on each dataset and for each set of n-grams, the logistic regression achieved most of the above-average values (33/36), followed by Naive Bayes with 25/36 and SVM with 22/36. The least favorable results were achieved with the random forest model with only 5/36 above-average predictions obtained for each dataset and n-gram.