## 4 Experimental Results and Performance Analysis  
This section discusses the experimentation conducted in this research using straight configuration (single feature selection technique), describing the experimental setup in Table 2 and the outcomes of the model used in all trials. Classification accuracy (CA) is the ratio of correctly classified data to all input data. The confusion matrix (CM) generates a matrix that describes the model's performance. Table 3 represents the evaluation of the test split using a single feature selection technique in combination with LL, and its graphical representation is shown in Figure 5.  
The information given below in the table illustrates four critical terms from the confusion matrix: true positives (TP) are the number of tweets anticipated as positive and are perfectly positive; true negatives (TN) are the number of tweets anticipated as negative and are perfectly negative; false positives (FP) are the number of tweets anticipated as positive but are perfectly negative, and false negatives (FN) are the number of tweets anticipated as negative but are perfectly positive. The matrix's accuracy is determined by multiplying the proportion of correctly identified occurrences by the total number of classified examples. In trials, a model's performance is evaluated using the accuracy metric. Along with accuracy, precision by averaging the classifier's false positives and false negatives and by averaging the classifier's and F1-score's weighted harmonic averages were calculated. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Instances** & 3085 & 3085 & 3085 & 3085 \\ \cline{2-5}
**Dataset** & **Attributes** & 44 & 44 & 44 & 44 \\ \cline{2-5}  & **Training Split** & 66\% & 70\% & 80\% & 90\% \\ \cline{2-5}  & **Testing Split** & 34\% & 30\% & 20\% & 10\% \\ \hline \multicolumn{5}{c}{WEKA} \\ \multicolumn{5}{c}{Filters} \\ \multicolumn{5}{c}{Unsupervised} \\ \multicolumn{5}{c}{Attribute} \\ \multicolumn{5}{c}{Tweet to Lexicon Feature Vector (a batch filter that calculates attributes for a tweet using multiple affective lexicons)} \\ \multicolumn{5}{c}{Text Index: 2} \\ \multicolumn{5}{c}{Lower Case: True} \\ \multicolumn{5}{c}{Reduce Repeated Letters: True} \\ \multicolumn{5}{c}{Standardize URLs and @ User Mentions: True} \\ \multicolumn{5}{c}{Tokenizer: Tweet NLP Tokenizer} \\ \multicolumn{5}{c}{Stemmer: Null Stemmer} \\ \multicolumn{5}{c}{Stop Words Handler: Null} \\ \multicolumn{5}{c}{Use MPQA: True} \\ \multicolumn{5}{c}{Use Bing Liu: True} \\ \multicolumn{5}{c}{Use AFINN: True} \\ \multicolumn{5}{c}{Use S140: True} \\ \multicolumn{5}{c}{Use NRC Hash Sentiment: True} \\ \multicolumn{5}{c}{Use NRC 10: True} \\ \multicolumn{5}{c}{Use NRC 10 Expanded: True} \\ \multicolumn{5}{c}{Use NRC Hash Emo: True} \\ \multicolumn{5}{c}{Use Sentiment Wordnet: True} \\ \multicolumn{5}{c}{Use Emoticons: True} \\ \multicolumn{5}{c}{Use Negation: True} \\ \multicolumn{5}{c}{Debug: False} \\ \multicolumn{5}{c}{Do Not Check Capabilities: False} \\ \multicolumn{5}{c}{WeKA} \\ \multicolumn{5}{c}{Classifiers} \\ \multicolumn{5}{c}{Functions} \\ \multicolumn{5}{c}{LibLINEAR (a wrapper class for the LL classifier)} \\ \multicolumn{5}{c}{SVM Type: L2-Regularization L2-Loss Support Vector Classification (dual)} \\ \multicolumn{5}{c}{Batch Size: 100} \\ \multicolumn{5}{c}{Bias:1.0} \\ \multicolumn{5}{c}{Cost:1.0} \\ \multicolumn{5}{c}{Debug: False} \\ \multicolumn{5}{c}{Do Not Check Capabilities: False} \\ \multicolumn{5}{c}{EPS:0.001} \\ \multicolumn{5}{c}{Epsilon Parameter:0.1} \\ \multicolumn{5}{c}{Maximum Number of Iterations:1000} \\ \multicolumn{5}{c}{Normalize: False} \\ \multicolumn{5}{c}{Decimal Place: 2} \\ \multicolumn{5}{c}{Probability Estimates: False} \\ \multicolumn{5}{c}{Weights: -} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Run information (single feature selection technique).