## 4 Performance Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Scheme** & _P(neg1 test)_ & _P(pos1 test)_ & **Polarity** \\ \hline \multirow{4}{*}{1} & _TF_ & \(-\)_109.0824_ & \(-\)_112.5455_ & _negative_ \\  & _TF-IDF_ & \(-\)_131.0328_ & \(-\)_135.1276_ & _negative_ \\  & _DF_ & \(-\)_104.6422_ & \(-\)_106.6193_ & _negative_ \\  & _PSW_ & \(-\)_92.5987_ & \(-\)_95.7893_ & _negative_ \\  & _CDS-C_ & \(-\)_92.7449_ & \(-\)_96.1055_ & _negative_ \\  & _TF_ & \(-\)_40.8930_ & \(-\)_40.6772_ & _positive_ \\  & _TF-IDF_ & \(-\)_47.3391_ & \(-\)_46.0011_ & _positive_ \\
2 & _DF_ & \(-\)_40.8216_ & \(-\)_39.3454_ & _positive_ \\  & _PSW_ & \(-\)_39.4379_ & \(-\)_37.6068_ & _positive_ \\  & _CDS-C_ & \(-\)_40.1410_ & \(-\)_38.9358_ & _positive_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The decisions of polarity based on the sentiment values. TF: Term Frequency, TF-IDF: Term Frequency and Inverse Document Frequency, DF: Document Frequency, PSW: Part-of-Speech feature-based Weighting, CDS-C: Category Discriminative Strength with Chi-Square model. Figure 4: The comparisons of the schemes on various metrics. (**a**) accuracy; (**b**) precision; (**c**) recall; (**d**) F1-score. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tested Tweet** & **Content** & **Indicator** \\ \hline \multirow{2}{*}{1} & _‘ok I’m sick and spent an hour sitting in the shower cause I was too sick_ & \(0\) \\  & _to stand and held back the puke like a champ. BED now’_ & \(0\) \\
2 & _‘@[PERSON] vep Mornings are the Best! nighttime is chill time’_ & \(4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Two examples of tested tweets. Figure 5 presents the performance of the five schemes in terms of the accuracy with the balanced training dataset, but biased tested data. As observed from Figure 5a,b, the proposed approach always produces the highest accuracy compared to the other four schemes regardless of the polarity of the tested documents. It is challenging to analyze biased tweets using a training dataset of a limited size. Since a specific training dataset has an inclination on the tested instances of a certain polarity, a classifier trained using it may not be robust enough. Note that the proposed fine-grained clustering strategy accurately defines the boundary of the features, and groups the features into different clusters. Also, the discriminative features are effectively aggregated considering its significance and distribution. As a result, the proposed scheme can allow high classification accuracy in processing biased tested data. Observe from Figure 5a that the DF scheme achieves the second-highest accuracy, while TF shows the lowest accuracy. However, they display opposite performances for the document of negative polarity as shown in Figure 5b. This is because DF and TF weight the features based on the absence and frequency, respectively. Thus, the schemes are suitable for specific tested data, while their performances are significantly affected by the polarity of the tested data. In order to validate the robustness and efficiency of the schemes, the experiments are conducted with the tested dataset formed with the documents of random polarity. This set of experiments is complementary to the second group in evaluating biased tested tweets. Observe from Figure 6 that the proposed scheme consistently shows the highest performance in terms of all the four metrics. An unreasonable assignment of the weight to the features causes inaccurate decision of the polarity. Since the proposed adaptive weighting strategy computes the weight considering the inter-category and intra-category relevance, dependency, and distribution of the features, it can exhibit robust performance. This confirms that the proposed scheme is effective regardless of the dataset with respect to the size and polarity of the tested dataset. Figure 5: The comparisons of accuracy with the tested documents of different polarity. (**a**) Positive; (**b**) Negative.