## 5 Results and Discussion
### Labeled Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Corpus Language** & **Lexicon** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline \multirow{3}{*}{CorpusCine} & \multirow{2}{*}{English (translated)} & TextBlob & **0.6975** & 0.5828 & **0.6350** & **0.5939** \\  & & VADER & 0.5966 & 0.5685 & 0.5822 & 0.5764 \\  & & Spanish lexicon & 0.5500 & **0.6084** & 0.5900 & 0.5649 \\ \hline \multirow{3}{*}{PaperReviews} & \multirow{2}{*}{English (translated)} & TextBlob & 0.6645 & 0.6062 & 0.6340 & 0.5271 \\  & & VADER & **0.6918** & **0.6628** & **0.6770** & **0.6007** \\ \cline{1-1}  & & Spanish lexicon & 0.6382 & 0.6337 & 0.6359 & 0.5921 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance of lexicon-based approaches for CorpusCine and PaperReview datasets and their machine-translated English versions. For the Spanish lexicon, we use the available in [[https://www.kaggle.com/general/88685](https://www.kaggle.com/general/88685)]([https://www.kaggle.com/general/88685](https://www.kaggle.com/general/88685)) (accessed on 21 March 2021). The best performance is highlighted in bold. Conversely, regarding the results (Table 6), we notice that, similar to previous outcomes, the best F1 scores come from supervised and hybrid approaches. However, we remark a considerable low yielding from our proposal and the deep learning approach introduced in [58]; in fact, both methods are defeated by the lexicon-based algorithms (see Table 5). Concerning the deep learning-based results, we argue that such low performance can be caused by a lack of generalization (overfitting). Now with respect to our hybrid proposal, we explain the undesired results in two regards. First, like the results for the CorpusCine dataset, the training step does not have access to the actual labels; therefore, the behavior of our approach can be enhanced by using partially labeled corpora. Second, we observe that the results from our method are skewed towards positive labels (the precision is greater than recall), which is caused for a discrepancy between the paper is evaluated and the way the review is written [60]. Finally, aiming to evaluate the behavior of our hybrid proposal in scenarios with limited labeled data, we carry out an additional experiment where we vary the number of labels. Let be the ratio of available labeled data. We split the CorpusCine review dataset into two groups. The first, \(X_{l}\) is conformed by \(pN\) (being \(N\) the number of documents in such corpus) samples with their corresponding true labels \(\mathbf{y}_{l}\in\{0,1\}^{pN}\); conversely, the second group \(X_{u}\) contains \(N(1-p)\) unlabeled documents. Hence, we apply the LRSentiA algorithm over the unlabeled corpus \(X_{u}\) to generate pseudo-samples. LRSentiA classifies such labels into five groups (Very-high, High, Low, Very-low, and zero) according to their confidence. Then, we conform the sets \(X_{t}\), and \(\mathbf{y}_{t}\) by concatenating the samples and labels from the Very-high and High categories together with the labeled data \((X_{l},\mathbf{y}_{l})\). \(X_{t}\) and \(\mathbf{y}_{t}\) is then used to feed a classification algorithm to predict the label for documents belonging to low, very-low, and zero confidence. For each value of \(p\), we use a five-fold validation scheme, where the sets \(X_{l}\), and \(X_{u}\) are conformed randomly. The performance is measured in terms of the overall accuracy. Figure 2 shows the mean and standard deviation for the accuracy of our proposal as a function of the ratio of labeled data. For specific testing, we compare a logistic regression classifier and a Support vector machine since they achieved the highest performances in previous experiments (see Table 6). From Figure 2, we remark that in most cases, the performance of our approach increases as there are more labeled data. Besides, we highlight that with the 40% of labeled data, our