## 2 Related Work  
Sentiment analysis is a hot topic in the natural language processing context that comprises the analysis of people's opinions and sentiments from written language [9]. Sentiment analysis has successfully applied to different scenarios such as customer opinions [25], enterprise marketing [26], and public opinion about local and international political conflicts [5]. In the literature, we recognize several works regarding political disputes. For example, authors in [27] perform a sentiment analysis from tweets during the 2016 primary debates aiming to assess the opinions' polarity about [PERSON]. Alike, in [28] a sentiment analysis is employed to carry out a bias detection in Western news about the Palestinian/Israeli conflict. On the other hand, we recognize additional sentiment analysis approaches to analyze conflicts such as the Iran-USA nuclear deal [29], the USA-China trade war [30; 31], and the problem of Syrian refugees [9]. Conversely, concerning the Colombian conflict, we notice a lack of sentiment analysis-based studies to describe such conflict and its stages (Conflict, peace agreement, and post-conflict). We only identified two relevant works. The first is the proposed in [32], which uses a crawled dataset from Twitter to analyze the perception of the post-conflict in Colombia from two perspectives: local and foreign perception. Besides, [33] uses a qualitative approach to determine the semantic orientation of information captured from Twitter, aiming to evaluate Norway's contribution to the peace process in Colombia. On the other hand, we broadly identified three methods for measuring semantic orientation, namely, lexicon-based models, supervised learning methods, and deep learning approaches. Lexicon-based methods comprise the use of a dictionary, where each word is labeled as positive or negative. Then, a sentence is tokenized, and it is matched with the words in the dictionary, aiming to compute the sentiment polarity [34]. However, one of the main drawbacks of lexicon-based methods is that applying one or another dictionary could lead to different polarity assessments [35]. In contrast to lexicon-based methods, machine learning-based approaches use manually labeled training data to code relevant patterns related to the sentiment analysis. Supervised learning methods usually outperform dictionary-based systems, and in fact, several relevant sentiment analysis models are built from machine learning algorithms [36]. Unlike previous methods, which should spend considerable time defining handcrafted features and performing feature engineering, deep learning models automatically perform such tasks [15]. Moreover, deep learning has exhibited important advances in several areas, including Computer Vision [37] and NLP, where the application of deep learning to sentiment analysis has become popular [38]. However, one of the main drawbacks of these methods (machine learning and deep learning) for sentiment analysis is the requirement of a labeled dataset, where the actual label (gold standard) is provided for each document in the corpus. In different scenarios, collecting such true labels is a non-trivial task because it can be tedious, expensive, or even unfeasible [39]. Several self-supervised approaches have been presented to deal with sentiment analysis problems with no labels. Such methods are mainly based on the combination of a lexicon and machine learning algorithms [40; 41]. Accordingly, we notice that most of the works related to self-supervised learning for sentiment analysis are developed for documents written in the English language. However, the majority of documents related to the Colombian conflict are Spanish-written. Spanish is one of the most spoken languages in the world (more than 500 million speakers), just behind English, Chinese, and Hindi [42]. However, despite the importance of the Spanish language, we identify that the area of sentiment analysis in the Spanish language is not as well-resourced as in the English language [43].