## 4 Results
### Statistics and Trends
#### 4.5.2 Overview of the Trends by Methods  
The average accuracy of all models trained and tested on the collected datasets obtained with the analysis with LIWC, n-grams, or the analysis by combining these techniques for all selected sets of n-grams are shown in Table 14.  
Analysis combining n-grams and LIWC achieved better average results for almost all selected sets of n-grams compared to the analysis using n-grams only. Consequently, the overall average accuracy obtained by this approach is also higher than the average results obtained by the n-gram analysis for each of the three datasets, DS1, DS2, and DS3. Considering the average accuracies obtained for all sets of n-grams, the analysis with n-grams and LIWC achieves the maximum on DS2 using the analysis of unigrams and bigrams (74.12%) and with the analysis of bigrams on DS3 (75.60%). Dataset DS1 was best classified using unigrams and bigrams (78.51%). By comparing the average accuracies obtained using all machine learning models on all selected sets of n-grams and LIWC, the analysis using a combination of (1,2)-grams and LIWC gives the most accurate results (75.71%). This is followed by the analysis with 2-g and LIWC and (2,3)-grams and LIWC with the average accuracy of 75.29% and 75.19%, respectively, on all models and datasets. Table 14 also shows that the models trained and tested on DS1 data give the best predictions compared to other datasets for each of the chosen data processing methods. Average model accuracies were also calculated for models trained on the combined dataset DS3 and tested individually on DS1 or DS2 data, which is shown in Table 15. The overall average results obtained by testing the DS3 model on DS1 or DS2 data are also shown. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **All Models** & \\ \cline{2-6}  & & **DS1** & **DS2** & **DS3** &
\begin{tabular}{c} **The Average Accuracy Obtained by Training and** \\ **Testing Models on Datasets DS1, DS2 and DS3** \\ \end{tabular} \\ \cline{2-6}  & **LIWC** & 77.45 & 73.13 & 73.85 & 74.81 \\ \hline \multirow{2}{*}{**1,1**} & n-grams & 74.15 & 68.54 & 70.15 & 70.95 \\ \cline{2-6}  & n-grams + LIWC & 76.13 & 73.16 & 73.15 & 74.15 \\ \cline{2-6}  & n-grams & 78.51 & 68.41 & 71.45 & 72.79 \\ \cline{2-6}  & n-grams + LIWC & 77.75 & 74.12 & 75.25 & 75.71 \\ \hline \multirow{2}{*}{**2,2**} & n-grams & 76.81 & 69.68 & 71.75 & 72.75 \\ \cline{2-6}  & n-grams + LIWC & 76.77 & 73.50 & 75.60 & 75.29 \\ \hline \multirow{2}{*}{**1,3**} & n-grams & 77.95 & 68.08 & 71.00 & 72.34 \\ \cline{2-6}  & n-grams + LIWC & 77.55 & 73.59 & 74.25 & 75.13 \\ \hline \multirow{2}{*}{**2,3**} & n-grams & 75.49 & 70.43 & 69.75 & 71.89 \\ \cline{2-6}  & n-grams + LIWC & 78.31 & 72.86 & 74.40 & 75.19 \\ \cline{2-6}  & n-grams & 68.69 & 58.89 & 64.70 & 64.09 \\ \cline{2-6}  & n-grams + LIWC & 75.72 & 70.36 & 73.20 & 73.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Overview of trends by methods on 3 selected datasets (colored values indicate the maximum accuracy obtained using a particular data processing method on each of the given datasets). In this case as well, the combined analysis with n-grams and LIWC achieved the highest average maximum for both datasets. When testing the DS3 models on DS1 data, the average maximum of 80.19% was obtained by analyzing unigrams and bigrams and LIWC. While testing these models on the DS2 data using the same method, the maximum average accuracy of the models was achieved by analyzing LIWC and trigrams (75, 21%). Considering the overall average obtained by the testing models trained on DS3 and tested on DS1 and DS2 datasets, the best average accuracy was achieved using bigram and LIWC (76.40%). The second best results were achieved by analyzing unigrams and LIWC (75.98%) and (1,3)-grams and LIWC (75.73%). Analyses exclusively with LIWC or n-grams achieved worse average results on all models compared to the combined analysis.