## 1 Introduction
Unlike traditional methods, which use handcrafted features, deep learning models automatically create the required features for the learning process [15]. The most popular deep learning architecture includes Convolutional neural networks-(CNN) [16], Long Short-Term Memory-(LSTM) [17], and Attention-based networks [18]. Still, most deep learning approaches demand a lot of data to guarantee a good generalization performance. The above drawback can be minimized by using transfer learning techniques. Transfer learning uses domain-specific data to fine-tune the pre-trained deep learning models; a common practice is to connect fully connected layers to pre-trained language models (based, for example, on words embedding, CNN, LSTM) [19]. Thereby, the training data is used to compute the parameters belonging to such fully connected layers, while the parameters related to the language model remain untouched [20]. In this regard, the number of trainable parameters is reduced considerably, which gives two crucial benefits: the time spent in the training process is reduced, and it is possible to use small datasets without increasing the probability of overfitting [21]. Nevertheless, despite the transfer learning benefits, the accuracy of sentiment relies on the availability of labeled corpora, and the quality of their labels, which in some practical scenarios can be problematic due to labeling can be tedious, complex, expensive, or even unfeasible [22]. Aiming to tackle sentiment analysis scenarios with limited labels or unlabeled datasets, we recognize two common practices. The first consists of collecting labels from multiple annotators throughout crowd-sourcing platforms such as Amazon Mechanical Turk ([[https://www.mturk.com/](https://www.mturk.com/)]([https://www.mturk.com/](https://www.mturk.com/)), accessed on 31 March 2021). The attractiveness of these platforms consists in that the labeling processes can be divided into multiple workers obtaining labels (possibly noisy) at a low-cost [22]. Such noisy labels are then used to train a sentiment analysis based on learning from crowds techniques [23]. The second alternative to building sentiment classification systems in presence of limited datasets is to use self-supervised learning methods, which intend to generate pseudo-labels that are used to train a supervised learning model [24]. For sentiment analysis, lexicon-based models are usually employed combined with filtering stages in order to obtain a set of accurate pseudo-labels for training a supervised learning algorithm [14]. Thereby, in this work, we present a self-supervised approach for sentiment analysis in unlabeled Spanish text. The proposed self-supervised methodology is a hybrid methodology that combines lexicon-based and classification methods aiming to build a sentiment analysis system without using any labeled data. Our approach is an extension of SSentiA [14] aiming to deal with Spanish-written texts. Our method comprises three stages. First, it employs a machine translation to build an English version of Spanish corpora. Second, a lexicon-based algorithm is applied to build a set of pseudo-labels with corresponding confidence scores. Finally, a binary classification model is trained with highly-confident pseudo-labels. Such a classifier is used to measure semantic orientation (positive or negative) of the texts whose pseudo-label was categorized with low confidence. We test our proposal over two labeled corpora, namely, CorpusCine and PaperReviews, aiming to validate the performance of our approach in a controlled scenario. Besides, we employ the developed model to assess the polarity in college media that have covered the Colombian conflict, presenting new knowledge about the peace process based on the visibility of the country's college media as a critical scenario of interpretation and as a laboratory of quality production. Achieved results show how our method can predict sentiment polarities from unlabeled or partially labeled data. The remainder is organized as follows.