## 4 Results
Additionally, it has the lowest Hamming loss of 0.11, indicating the smallest fraction of unclassified labels. Overall, BOSentiment is the most accurate and precise classifier among the listed options. Looking at the table, we can see that the next best classifier after BOSentiment is BoSA, which has a precision score of 0.78 and a relatively low Hamming loss of 0.22. BoSA has the highest precision scores for each sentiment label, and its \(F_{1}\) score is also relatively high compared to the other classifiers. However, it has a higher mean squared error and standard deviation compared to BOSentiment, indicating a higher amount of error and variance in its predictions. The next best classifiers after BoSA are Decision Tree and k-Nearest Neighbors, both with precision scores of around 0.5 and a Hamming loss of 0.32. Decision Tree has higher precision scores for negative and neutral sentiment, while k-Nearest Neighbors has higher precision scores for positive sentiment. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Classifier** & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \begin{tabular}{c} **\(F_{1}\) Score** \\ **(Class)** \\ \end{tabular} & \begin{tabular}{c} **Avg. \(F_{1}\) Score** \\ **(Model)** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy** \\ **Score** \\ \end{tabular} & \(\sigma_{M}\) * & \(\sigma\) * &
\begin{tabular}{c} **Hamming Loss** \\ **Score** \\ \end{tabular} \\ \hline Random Forest & \(-1\) & 0.58 & 0.98 & 0.73 & & & & & & \\  & 0 & 0.25 & 0.01 & 0.02 & 0.44 & 0.58 & 0.25 & 0.32 & 0.32 \\  & 1 & 0.50 & 0.07 & 0.12 & & & & & \\ \hline Bernoulli Naive Bayes & \(-1\) & 0.57 & 0.98 & 0.72 & & & & & & \\  & 0 & 0.00 & 0.00 & 0.00 & 0.42 & 0.57 & 0.88 & 0.18 & 0.43 \\  & 1 & 1.00 & 0.03 & 0.05 & & & & & \\ \hline Complements NB & \(-1\) & 0.61 & 0.37 & 0.46 & & & & & & \\  & 0 & 0.33 & 0.36 & 0.34 & 0.40 & 0.38 & 0.37 & 0.85 & 0.43 \\  & 1 & 0.21 & 0.44 & 0.28 & & & & & \\ \hline Multinomial NB & \(-1\) & 0.59 & 0.61 & 0.60 & & & & & & \\  & 0 & 0.27 & 0.30 & 0.28 & 0.47 & 0.47 & 0.33 & 0.70 & 0.43 \\  & 1 & 0.39 & 0.27 & 0.32 & & & & & \\ \hline Decision Tree & \(-1\) & 0.61 & 0.71 & 0.66 & & & & & & \\  & 0 & 0.34 & 0.28 & 0.31 & 0.49 & 0.52 & 0.37 & 0.71 & 0.32 \\  & 1 & 0.30 & 0.22 & 0.25 & & & & & \\ \hline k-Nearest Neighbors & \(-1\) & 0.61 & 0.66 & 0.63 & & & & & & \\  & 0 & 0.27 & 0.39 & 0.32 & 0.47 & 0.49 & 0.78 & 0.55 & 0.32 \\  & 1 & 0.50 & 0.08 & 0.14 & & & & & \\ \hline SVM & \(-1\) & 0.57 & 0.98 & 0.72 & & & & & & \\  & 0 & 0.00 & 0.00 & 0.00 & 0.42 & 0.57 & 0.35 & 0.17 & 0.35 \\  & 1 & 1.00 & 0.03 & 0.05 & & & & & \\ \hline BoSA & \(-1\) & 0.84 & 0.90 & 0.87 & & & & & & \\  & 0 & 0.75 & 0.73 & 0.74 & 0.78 & 0.78 & 0.47 & 0.72 & 0.22 \\  & 1 & 0.61 & 0.48 & 0.54 & & & & & \\ \hline BOSentiment & \(-1\) & 0.93 & 0.93 & 0.93 & & & & & \\  & 0 & 0.85 & 0.90 & 0.87 & 0.89 & 0.89 & 0.23 & 0.75 & 0.11 \\  & 1 & 0.84 & 0.77 & 0.80 & & & & & \\ \hline \hline \end{tabular} \({}^{a}\): Mean squared error on whole data. \({}^{b}\): Standard deviation on whole data.  
\end{table} 
Table 2: Performance comparison of different classifiers for tweet classification. BoSA and BOSentiment achieve the highest \(F_{1}\) score for all sentiment instances \((-1,0,1)\) and have the highest accuracy scores. Additionally, BOSentiment outperforms BoSA with a higher accuracy score. Bernoulli Naive Bayes and Random Forest have similar precision scores of around 0.57-0.58 and a relatively high Hamming loss of 0.43 and 0.32, respectively. Bernoulli Naive Bayes has the highest precision score for positive sentiment, while Random Forest has the highest precision scores for negative and neutral sentiment. Multinomial Naive Bayes and Complements Naive Bayes have the lowest precision scores among the classifiers listed in the table, both with a precision score of around 0.38 and a Hamming loss of 0.43. Complements Naive Bayes has the highest precision score for positive sentiment, while Multinomial Naive Bayes has the highest precision score for neutral sentiment.