## 4 Experimental Analysis and Results
### Implementation of Metaheuristic GA-Based Feature Selection
#### 4.6.1 Exploratory Analysis of Training and Test Sets  
After reading and cleaning the texts of the training and test sets, the results of their exploratory analysis are presented to understand meaningful relationships between their classes. It is very important to gain domain knowledge together with identifying whether there is bias in the labels of the dataset. Below are several figures and charts with detailed explanations. The following chart is one attempt to give an intuition of potentially different lengths of text. Figure 3 shows the distribution of text length in the training and test sets. Text length limits the density and richness of conveyed information used for better classification. \begin{table}
\begin{tabular}{l l l l} \hline
**Classifier Model** & **Feature** & **Vector** & **F1-Score without Feature Selection (\%)** & **F1-Score with Feature Selection (\%)** \\ \hline ETC & Unigram & CountVectorizer & 95.00 & 95.38 \\ ETC & Unigram & TfidfVectorizer & 95.20 & 95.38 \\ LRC & Unigram & CountVectorizer & 93.52 & 94.21 \\ RFC & Unigram & CountVectorizer & 95.38 & 95.38 \\ RFC & Unigram & TfidfVectorizer & 95.32 & 95.38 \\ \hline \end{tabular}
\end{table} 
Table 10: The results of the five configuration settings for the sarcasm task scenarios with and without the feature selection and optimization method. \begin{table}
\begin{tabular}{l l l l l} \hline
**Target** & **Sum\_sq** & **Df** & **F** & **PR (\textgreater{}F)** \\ \hline Sentiment & 130.569338 & 1.0 & 0.88228 & 0.347946 \\ Sarcasm & 896.219770 & 1.0 & 6.107121 & 0.013733 \\ Stance & 1221.586594 & 1.0 & 8.354293 & 0.003983 \\ \hline \end{tabular}
\end{table} 
Table 7: ANOVA test results of test set targets for the three topics. \begin{table}
\begin{tabular}{c c c c} \hline
**Classifier Model** & **Feature** & **Vector** & **F1-Score with Feature Selection (\%)** \\ \hline ETC & Unigram & CountVectorizer & 79.17 & 80.88 \\ ETC & Unigram & TfidfVectorizer & 77.12 & 77.38 \\ LRC & Unigram & CountVectorizer & 75.01 & 75.32 \\ RFC & Unigram & CountVectorizer & 75.81 & 77.26 \\ RFC & Unigram & TfidfVectorizer & 75.86 & 76.86 \\ \hline \end{tabular}
\end{table} 
Table 8: The results of the five configurations settings for the sentiment task scenario with and without the feature selection and optimization methods. \begin{table}
\begin{tabular}{c c c c} \hline
**Classifier Model** & **Feature** & **Vector** & **F1-Score with Feature Selection (\%)** \\ \hline ETC & Unigram & CountVectorizer & 86.12 \\ ETC & Unigram & TfidfVectorizer & 86.26 \\ LRC & Unigram & CountVectorizer & 82.21 \\ RFC & Unigram & CountVectorizer & 86.51 \\ RFC & Unigram & TfidfVectorizer & 86.22 \\ \hline \end{tabular}
\end{table} 
Table 9: The results of the five configuration settings for the augmented sentiment task scenario with and without the feature selection and optimization method. Figure 3 shows that the distribution of text length in the training and test sets is modeled as a normal distribution, and approximately both sets have the same distribution. Moreover, it shows that most tweets have 50-250 words, which is the richness of conveyed information. However, the density of text length for each class label of the training and test sets is significant for analyzing whether the distributions of text length do not differ from each other. Figure 4 demonstrates the density distribution of text length for each topic's class label of the training and test sets. Figure 4: Density distribution of text length for each topic’s class label of the training and test sets, respectively: (**a,b**) are the density distribution of sentiment classes; (**c,d**) are the density distribution of sarcasm classes; and (**e,f**) are the density distribution of stance classes. Figure 3: Distribution of text length: (**a**) the distribution of length texts in the training set and (**b**) the distribution of text length in the test set.