## 4 Results  
### Model Testing on DS1 (Climate Change Dataset)  
Four selected machine learning models were tested on the collected datasets (DS1 and DS2) and the combined dataset (DS3). Table 3 shows the results obtained from the analysis  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Abbrev. **} & **Description/Most** & **Words/** & **Internal** & **Internal** \\  & & **Frequently Used** & **Entries in** & **Consistency:** & **Consistency:** \\  & & & **Examples** & **Category** & **Cronbach’s \(\alpha\)** & **KR-20** \\ \hline \multicolumn{2}{c}{**Summary**} & & & & \\ \multicolumn{2}{c}{**Variables**} & & & & \\ \multicolumn{2}{c}{Word count} & WC & Total word count & - & - & - \\ \multicolumn{2}{c}{Cout} & \multicolumn{2}{c}{Clout} & Language of leadership, & - & - & - \\ \multicolumn{2}{c}{Authentic} & \multicolumn{2}{c}{Authentic} & \multicolumn{2}{c}{Perceived honesty,} & - & - & - \\ \multicolumn{2}{c}{} & & genuineness & & & \\ \multicolumn{2}{c}{Emotional tone} & \multicolumn{2}{c}{Tone} & Degree or positive & - & - & - \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{(negative) tone} & & & \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{WPS} & Average words per & - & - & - \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{sentence} & & & \\ \multicolumn{2}{c}{Big words} & BigWords & Percent words 7 letters & - & - & - \\ \hline \multicolumn{2}{c}{**Linguistic**} & \multirow{2}{*}{Linguistic} & \multirow{2}{*}{4933} & \multirow{2}{*}{0.36} & \multirow{2}{*}{1.00} \\
1 st person singular & i & I, me, my, myself & 6/74 & 0.49 & 0.85 \\  
using LIWC, n-grams, and the combination of n-gram analysis with LIWC (by combining LIWC with all n-gram sets ranging from unigrams to trigrams). The notation of n-gram records indicates their range (e.g., 1,2-g represents unigrams and bigrams). Accuracy, precision, and response were used to evaluate the performance of the models. For each model, maximum accuracies obtained by employing LIWC analysis (green), n-grams (blue), and the combined approach analysis (red) are indicated while the largest of the three values is in bold (the same notation applied to the highest response and precision obtained using all the models). Analyzing the DS1 dataset with LIWC, the highest accuracy of 78.05% was achieved using random forest. A slightly better performance of the models was obtained using n-grams; more precisely, the maximum accuracy of 81.47% is given by the multinomial Naive Bayes model on unigrams and bigrams. The logistic regression follows with 80.68% accuracy in the analysis of unigrams, bigrams, and trigrams. Somewhat lower performance is given by the SVM and random forest models with an accuracy of 78.8% and 75.04% on the range of (1,2)-grams and (1,3)-grams. In addition, the accuracy of their predictions through other combinations of n-grams is lower than that obtained with analysis using LIWC for the same models while multinomial Naive Bayes and logistic regression gave better results during an analysis with n-grams compared to an analysis with LIWC. Furthermore, when analyzing DS1 by combining the n-gram and LIWC techniques, the models mostly achieved better results compared to using the other two approaches. The best performance is again given by the multinomial Naive Bayes model tested on (1,2)-grams and LIWC with an accuracy of 81.47%, which was also achieved using exclusively n-gram analysis. This is followed by logistic regression with an accuracy of 79.56% tested on the range of n-grams from unigrams to trigrams, which is slightly lower performance compared to the same model when analyzing n-grams, which achieved the maximum accuracy of 80.68%. By comparing the analysis of DS1 using n-grams and n-grams in combination with LIWC, improvements in model performance have been achieved using the combined