## 5 Results and Discussion
### Colombian Conflict Dataset  
As a final experiment, we apply our proposal to each document in the Colombian conflict dataset aiming to understand the narrative of the war from the journalism students' perspective. Aiming to validate the obtained results, we use the qualitative labels explained in Section 4.1.2. However, we note that such labels are not given for each document but for each media. Accordingly, to perform a fair comparison, we use the following procedure. Let \(X_{m}=\{x_{1}^{(m)},\ldots,x_{N_{m}}^{(m)}\}\) a set containing \(N_{m}\) documents corresponding to the \(m\)-th media. We use our hybrid method to obtain the sentiment polarity \(\mathbf{y}_{m}=[y_{1}^{(m)},\ldots,y_{N_{m}}^{(m)}]\) for each document in \(X_{m}\). Accordingly, the polarity of \(m\)-th media is computed as:  
\[P(X_{m})= \begin{cases}1,&\sum_{i=1}^{N_{m}}y_{1}^{(m)}\geq 0\\ 0,&\text{In other case}\end{cases} . \tag{4}\]  
Table 7 shows a comparison between the semantic orientation obtained by using our approach under four configurations (employing four classification algorithms: LR, SVM, RF, and NB) and a set of qualitative labels, which are obtained using the identification of lexical worlds [47] (see Section 4.1.2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Media**} & \multirow{2}{*}{**Qualitative Polarity**} & \multicolumn{5}{c}{**Proposal**} \\ \cline{3-6}  & & **RL** & **SVM** & **RF** & **NB** \\ \hline Universidad de Ibague & P & P(0.8000) & P(0.7888) & P(0.7666) & P(0.8333) \\ Universidad del Valle & P & N(0.1016) & N(0.1016) & N(0.1016) & N(0.1016) \\ Universidad de la Sabana & P & P(0.7651) & P(0.8066) & P(0.8259) & P(0.8701) \\ Unimmuto & P & P(0.7199) & P(0.7317) & P(0.8579) & P(0.8086) \\ Jorge Tadeo Lozano & P & P(0.7101) & P(0.8412) & P(0.8941) & P(0.8981) \\ Santiago de Cali & P & N(0.4772) & N(0.4512) & N(0.4610) & N(0.4090) \\ Universidad del Rosario & P & P(0.5614) & P(0.6359) & P(0.6052) & P(0.6403) \\ Universidad Autonoma de & P & P(0.5390) & P(0.5306) & P(0.6170) & P(0.7142) \\ Bucaramanga & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sentiment analysis for the Colombian conflict dataset. We compare the behavior of our proposal with qualitative labels for each media. For our approach, we present the predicted polarity and the ratio of documents whit positive semantic orientation. Figure 2: Accuracy of our approach (LR and SVM) as a function of the number of labeled data. From Table 7, we first notice that according to the qualitative assessment, all of the considered media expresses positive aspects of the Colombian conflict and its stages. Now, regarding the result of our self-supervised approach, we note a similar tendency, given that most of the media are categorized as positives; such outcomes are consistent when we vary the classification scheme (RL, SVM, RF, and NB), obtaining an overall accuracy of 75%. Specifically, we remark that for media Universidad del Valle and Santiago de Cali the sentiment orientation predicted for our approach differs from the qualitative labels; hence, to explain such outcomes, we analyze each media individually. For the Universidad del Valle media, we observe it contains 60 documents, and only 26 were categorized as very-high or high confidence. The above indicates that the supervised learning approach was trained with 26 samples, which indicates that the overfitting causes the results obtained. Conversely, regarding the Santiago de Cali media, we notice that despite the predicted label being negative, the ratio of documents with a positive semantic orientation is close to 0.5, which indicates the robustness of our approach to recognizing the polarity of documents related to the Colombian conflict. On the other hand, the results are complemented by the lexicometric approach. College journalism narrated the conflict with a positive stance; this is reflected in the need to understand Colombia's conflict and propose solutions and scenarios of agreement.