## 5 Results and Discussion  
### Labeled Datasets  
As we established in Section 3.1, we use a machine-translation approach to take advantage of the English resources; thus, it is crucial to verify that the documents' semantic orientation is not affected by the translation process [51; 52]. In this regard, we perform an initial experiment aiming to evaluate the machine-translation impact for sentiment analysis. For such an experiment, we use the CorpusCine and PaperReviews that review dataset, each dataset since these corpora contain the actual labels. We build two sentiment analysis systems (based on machine learning algorithms) for each corpus. The first uses the documents in their original language (Spanish), while the second employs an English-translated version of such documents. Each document is parametrized by using unigram and bigram-based term frequency-inverse document frequency. We use a five-fold validation scheme, where the performance is assessed in terms of the overall accuracy. Table 3 shows a comparison of  
Figure 1: Validation procedure using some state-of-the-art lexicon-based methods. a machine learning-based model for sentiment analysis trained by using both original and translated corpora. From Table 3, we remark that there are no significant differences between the performance (in terms of overall accuracy) of sentiment analysis systems from Spanish and English-translated corpora. Consequently, we experimentally demonstrate that the sentiment polarity is preserved after the English translation in most cases. Second, we perform a controlled experiment aiming to verify the capability of our proposal to compute the semantic orientation in the absence of labels. We use the CorpusCine and PaperReviews reviews dataset described previously. We remark that these datasets contain the true semantic orientations; nevertheless, such labels are only used to compute performance metrics. As we pointed out in Section 3.2 our approach uses a lexicon-based algorithm (LRSentiA) to generate pseudo-labels that are then used to train a machine learning model. Besides, LRSentiA computes the confidence score for each pseudo-label Equation (2), where it has been established that there exists a relation between such confidence scores and the prediction accuracy [14]. Accordingly, we perform a first experiment aiming to confirm previous affirmation experimentally. Table 4 shows the prediction accuracy compared with the confidence groups defined in Equation (3). From Table 4, it is possible to notice that, for both labeled datasets, there exists a significant relationship between the confidence scores and the prediction accuracy, the Very-high confidence group is related to the highest prediction accuracy. In contrast, the lowest accuracy corresponds to the Zero confidence group. We compute the Pearson coefficient between the prediction accuracy and the mean confidence score in each group to support our observations. Hence, we obtain a value of 0.8853 for CorpusCine and 0.8999 PaperReviews, which indicates a strong linear relationship between the analyzed variables confirming our initial qualitative analysis. As a second experiment, we use lexicon-based algorithms for the CorpusCine dataset. We employ the well-known English dictionaries TextBlob and VADER, applied over an English version of the studied dataset. Further, we use the Spanish dictionary described