## 5 Results and Discussion
### Labeled Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Confidence Group** & **Confidence Score \(\rho\) Equation (2)** & **Accuracy \%** & **\# Reviews** \\ \hline \multirow{4}{*}{CorpusCine} & Very-high & \((0.33,1.00)\) & 74.23 & 788 \\  & High & \((0.24,0.33)\) & 68.23 & 318 \\ \cline{1-1}  & Low & \((0.14,0.24)\) & 61.90 & 504 \\ \cline{1-1}  & Very-low & \((0.01,0.14)\) & 58.06 & 856 \\ \cline{1-1}  & Zero & \((0.00,0.01)\) & 47.16 & 159 \\ \hline \multirow{4}{*}{PaperReviews} & Very-high & \((0.69,1.00)\) & 63.04 & 92 \\ \cline{1-1}  & High & \((0.51,0.69)\) & 48.27 & 29 \\ \cline{1-1}  & Low & \((0.34,0.51)\) & 46.66 & 45 \\ \cline{1-1}  & Very-low & \((0.01,0.34]\) & 51.25 & 80 \\ \cline{1-1}  & Zero & \((0.00,0.01]\) & 28.15 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction accuracy of LRSentiA for the defined confidence groups (Equation (3)) in a machine-translated version of CorpusCine and PaperReview datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original Corpora**} & \multicolumn{2}{c}{**Machine-Translated**} \\  & \multicolumn{2}{c}{**(Spanish)**} & \multicolumn{2}{c}{**Corpora (English)**} \\ \cline{2-5}  & **LR** & **SVM** & **LR** & **SVM** \\ \hline CorpusCine & \(82.30\pm 1.01\) & \(83.62\pm 1.06\) & \(81.70\pm 1.24\) & \(83.40\pm 0.51\) \\ PaperReviews & \(78.09\pm 1.01\) & \(79.52\pm 2.54\) & \(77.61\pm 2.04\) & \(76.66\pm 2.07\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of sentiment analysis performance (in terms of overall accuracy) between Spanish corpora and English-translated corpora. LR: logistic regression, SVM: support vector machines. in Section 4.2. From Table 5, we observe that CorpusCine, in terms of the F1 score, the dictionary TextBlob obtains the best performance. On the other hand, regarding PaperReview, we note that the VADER lexicon outperforms all its competitors. However, we observe that the lexicon-based methods exhibit a considerably low-performance in general terms. The above behavior is explained in the sense that dictionaries-based algorithms obtain their best yielding with highly polarity text; however, they are ineffective in assessing the sentiment orientation of cases with mixed opinions [14]. Finally, we test our hybrid proposal over the labeled datasets. Table 6 shows the performance of our approach compared with different methods (Unsupervised, supervised, and hybrid) to perform sentiment analysis in Spanish corpora. Analyzing the results for CorpusCine in Table 6, we first evidence that our proposal reaches the best performance by using a logistic regression classifier, which is remarkable due to it can only deal with linear-separable data. The above suggests a linear structure may exist in the features extracted from the documents. On the other hand, we note that the approaches with the worst performance are those based on unsupervised learning algorithms, which is not surprising. As it was argued below, such types of methods cannot measure documents with mixed opinions. Now, concerning the supervised learning methods, we note that the F1 score considerably outperforms unsupervised algorithms. Such behavior is due to supervised models extracting relevant patterns related to the sentiment orientation. Then, we remark that the works based on hybrid approaches (i.e., combining supervised and unsupervised techniques) [53; 54] achieve the best classification scores (F1 score, Accuracy). However, regarding our hybrid approach, we observe a significantly lower performance; in fact, it only outperforms the unsupervised models. To explain such an outcome, we highlight that unlike the approaches in [53; 54], our proposal does not use any information about the gold standard. Conversely, it generates pseudo-labels using the lexicon-based method LRSentiA, which is then used to train a supervised classifier. Therefore, we argue that the performance of our approach can be increased by providing limited labeled data.