Understanding student feedback is essential for informing pedagogical strategies and institutional decision-making in higher education. Sentiment analysis offers scalable mechanisms for extracting insights from open-ended student evaluations; however, many existing approaches prioritize technical performance without sufficient consideration of contextual and institutional constraints, particularly in underrepresented regions. This study proposes a context-aware framework for sentiment analysis of student feedback, designed to support educational decision-making within Latin American universities. Rather than introducing new algorithms, the framework systematically evaluates established machine learning and deep learning models through a multi-phase process that includes data preprocessing, Bayesian optimization, threshold calibration, and class balancing. The framework is validated using authentic Spanish-language student feedback collected from a public university in Peru. Experimental results indicate that while advanced models can achieve strong predictive performance, simpler and more interpretable approaches often provide comparable institutional value when deployment feasibility, computational efficiency, and transparency are considered. These findings highlight that marginal performance gains do not necessarily translate into meaningful advantages for routine educational use. Overall, this work contributes a replicable and resource-sensitive framework that bridges learning analytics research and practical educational application. By prioritizing contextual suitability and interpretability, the proposed approach enables higher education institutions to leverage student sentiment data as an actionable input for continuous improvement and evidence-based educational strategies.

The epidemiological outbreak of a novel coronavirus (2019-nCoV or Covid-19) in China, and its rapid spread, gave rise to the first pandemic in the digital age. Derived from this fact that has surprised humanity, many countries started with different strategies in order to stop the infection. In this context, one of the greatest challenges for the scientific community is monitoring (real time) the global population to get immediate feedback of what is happening with the people during this public health contingency. An alternative interesting and affordable for the materialization of the aforementioned are the social networks. In a social network, the persons can act as sensors/information not only of personal data but also data derived from their behavior. This paper aims to analyze the publications of people in Mexico using a Text Mining approach. Specifically, Mexico City is presented as a case study to help understand the impact on society of the spread of Covid-19.

The aim of this study is to examine the use of NLP in an extensive amount of satisfaction feedback in the process and experience of university students from the engineering faculty. The NLP process used was sentiment analysis, which uses artificial intelligence for the extraction of textual data and can classify it as positive, neutral, and negative. The results of the NLP were compared with the manual analysis of the same feedback, finding differences in the results of both methods. Finally, we conclude that the sentiment analysis method is feasible to implement in the context of measuring student satisfaction, and it can become an effective tool with the possibility of working in real-time to generate useful information for making university decisions.