## 4 Results
### Statistics and Trends  
#### 4.5.1 Overview of the Trends by Model  
In order to get a better insight into the performance of the used machine learning models, average values of each model obtained by testing on three selected datasets using LIWC analysis, n-grams and by combining n-gram analysis with LIWC was extracted (Table 12). The same was done with the models trained on the combined dataset DS3, and tested individually on the DS1 and DS3 datasets, which is shown in Table 13.  
The average accuracy of all models obtained with the analysis using a combination of n-grams and LIWC is higher than that of the one using only the n-gram analysis for each of the three datasets, DS1, DS2, and DS3. The analysis based on LIWC alone also achieves higher accuracy compared to the average results obtained by testing all models on different sets of n-grams. The average accuracy of predictions obtained for all models using the LIWC analysis generally gave less favorable results compared to the analysis with a combined approach (n-grams and LIWC) on datasets DS2 and DS3 and a maximum average accuracy of 77.45% when tested on dataset DS1. Table 13 compares average prediction accuracies obtained using all selected models and natural language processing techniques by training the model on the combined dataset, DS3, and testing individually on datasets DS1 and DS2. In this case as well, the analysis with n-grams on average gives less favorable results compared to the other data processing techniques used. The highest average accuracy for both datasets was achieved with the n-gram analysis combined with LIWC while the LIWC analysis also gives favorable results when tested on both datasets. The maximum average accuracies, considering the results of all DS3 models tested on the DS1 and DS2 datasets, were achieved using the analysis combining n-gram and LIWC and are 78.01% and 72.90%, respectively. \begin{table}
\begin{tabular}{c c c 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**} & \multicolumn{2}{c}{**All Models**} \\ \hline  & **DS3** & **DS3** & **DS3** & **DS3** & **DS3** & **DS3** & **DS3** & **DS3** & **DS3** & **DS3** & **DS3** \\  & **Models** & **Models** & **Models** & **Models** & **Models** & **Models** & **Models** & **Models** & **Models** & **Models** & **Models** \\  & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** & **Tested** \\  & **on DS1** & **on DS2** & **on DS1** & **on DS2** & **on DS1** & **on DS2** & **on DS1** & **on DS2** & **on DS1** & **on DS2** & **on DS1** & **on DS2** \\  & **Data** & **Data** & **Data** & **Data** & **Data** & **Data** & **Data** & **Data** & **Data** & **Data** & **Data** \\ \hline
**LIWC** & 81.54 & 72.04 & 81.54 & 70.38 & 77.31 & 71.58 & 71.38 & 70.33 & 77.94 & 71.08 \\ \hline
**n-grams** & 72.82 & 66.11 & 71.28 & 65.56 & 72.50 & 67.08 & 69.55 & 62.92 & 71.54 & 65.42 \\ \hline
**n-grams** & 75.51 & 72.78 & 79.53 & 73.54 & 79.49 & 72.43 & 77.50 & 72.85 & 78.01 & 72.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Overview of trends by model for combined dataset DS3 tested individually on datasets DS1 and DS2 (yellow color: maximum average accuracy obtained for each individual model and dataset; red color: maximum average accuracy obtained using all models on each individual dataset).