## 5 Experimental Evaluation
### Data Preparation and Evaluation Metrics
In details, we noticed that for **U**, **U**,**B**, **U**,**B**,**T** and **U**,**B**,**T**,**Q** feature patterns, the kNN classifiers obtained results that were comparable with the other classifiers. Instead, for feature patterns that did not include uni-grams, the kNN classifier obtained very poor results. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature Pattern** & **No. of Features** & **Accuracy** & **Precision** & **Recall** & **F1-Measure** \\ \hline \multirow{4}{*}{**U**} & 24,961 (100\%) & 78.29 & 78.74 & 78.29 & 78.36 \\  & 19,969 (80\%) & 78.69 & 79.3 & 78.69 & 78.83 \\  & 16,225 (65\%) & 78.64 & 79.07 & 78.64 & 78.71 \\  & 12,481 (50\%) & 78.14 & 78.67 & 78.14 & 78.25 \\ \hline \multirow{4}{*}{**B**} & 165,704 (100\%) & 60.37 & 73.73 & 60.37 & 62.75 \\  & 132,563 (80\%) & 60.42 & 75.13 & 60.42 & 63.17 \\  & 107,708 (65\%) & 60.66 & 74.7 & 60.66 & 63.33 \\  & 82,852 (50\%) & 60.69 & 74.67 & 60.69 & 63.35 \\ \hline \multirow{4}{*}{**T**} & 181,514 (100\%) & 39.79 & 81.8 & 39.79 & 42.27 \\  & 145,211 (80\%) & 39.08 & 82.16 & 39.08 & 41.49 \\  & 117,984 (65\%) & 39.53 & 81.79 & 39.53 & 42.16 \\  & 90,757 (50\%) & 39.48 & 81.18 & 39.48 & 42.11 \\ \hline \multirow{4}{*}{**Q**} & 168,482 (100\%) & 28.25 & 83.95 & 28.25 & 27.39 \\  & 134,786 (80\%) & 28.09 & 83.59 & 28.09 & 27.13 \\  & 109,513 (65\%) & 28.16 & 83.94 & 28.16 & 27.26 \\  & 84,241 (50\%) & 28.4 & 82.66 & 28.4 & 27.58 \\ \hline \multirow{4}{*}{**U,B**} & 190,665 (100\%) & 77.03 & 77.88 & 77.03 & 77.17 \\  & 152,532 (80\%) & 77.79 & 78.49 & 77.79 & 77.92 \\  & 123,932 (65\%) & 77.95 & 78.79 & 77.95 & 78.11 \\  & 95,333 (50\%) & 78.3 & 79.1 & 78.3 & 78.43 \\ \hline \multirow{4}{*}{**B,T**} & 347,218 (100\%) & 59.41 & 74.29 & 59.41 & 62.03 \\  & 277,774 (80\%) & 59.78 & 75.74 & 59.78 & 62.66 \\  & 225,692 (65\%) & 59.72 & 75.16 & 59.72 & 62.59 \\  & 173,609 (50\%) & 60.19 & 75.39 & 60.19 & 63.03 \\ \hline \multirow{4}{*}{**T,Q**} & 349,996 (100\%) & 38.95 & 82.91 & 38.95 & 41.28 \\  & 279,997 (80\%) & 37.95 & 82.65 & 37.95 & 40.08 \\  & 227,497 (65\%) & 38.41 & 82.92 & 38.41 & 40.74 \\  & 174,998 (50\%) & 38.32 & 82.07 & 38.32 & 40.67 \\ \hline \multirow{4}{*}{**U,B,T**} & 372,179 (100\%) & 76.12 & 77.28 & 76.12 & 76.3 \\  & 297,743 (80\%) & 76.6 & 77.63 & 76.6 & 76.78 \\  & 241,916 (65\%) & 77.58 & 79.03 & 77.58 & 77.82 \\  & 186,090 (50\%) & 77.23 & 78.34 & 77.23 & 77.42 \\ \hline \multirow{4}{*}{**U,B,T**} & 540,661 (100\%) & 76.03 & 77.36 & 76.03 & 76.18 \\  & 432,529 (80\%) & 75.45 & 77.64 & 75.45 & 75.61 \\ \cline{1-1}  & 351,430 (65\%) & 76.87 & 78.29 & 76.87 & 77.09 \\ \cline{1-1}  & 270,331 (50\%) & 76.94 & 78.53 & 76.94 & 77.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results for Random-Forest classifier. Nevertheless, notice that the other three tested classification techniques suffered for the absence of uni-grams in the feature sets as well, even though they behaved better than the kNN classifier. If we focus on results obtained by each classifier for feature patterns that contain uni-grams, it clearly appears that no advantage was obtained by combining uni-grams with other features. Looking at Table 4, we see that the Naive Bayes classifier obtained a very slight improvement; in contrast, looking at Tables 5-7, we can see a slight deterioration of accuracy, when comparing the **U** pattern with **U**, **B**, **U**,**B**,T** and **U**,**B**,T**,Q** combined patterns.