Satisfaction with teaching performance is an important measurement process in higher education institutions, for this reason, applying sentiment analysis to the opinions of university students through the support vector machine (SVM) Fine Gaussian supervised learning algorithm represents an important contribution to the academic literature. This article identifies the best classification algorithm according to performance parameters for predicting student satisfaction with teaching performance through sentiment analysis; the subsequent implementation of the research has the purpose of strengthening teaching practices, in addition to allowing continuous training of teaching for the benefit of student learning. This article has provided a compact predictive model, with literature review based on SVM and sentiment analysis techniques. Through the machine learning classification learner technique, it is identified that the SVM algorithm: Fine Gaussian SVM is the one with the best accuracy equal to 98.3%. Likewise, the performance metrics for the four classes of the model were identified, which have a sensitivity equal to 88.89%, a specificity of 98.04%, a precision of 99.21% and an accuracy of 98.85%.

Although it is true that artificial intelligence and data science have become key tools that contribute to the improvement of many processes, identifying patterns and contributing to decision making, however, there are environments in which they are not yet being using it relevantly and effectively. The objective of this study is to identify the relevant factors, based on the opinions expressed by the students through the social network Twitter regarding the perception of satisfaction with the teaching performance during the virtual learning environment. For which sentiment analysis and text mining are used under the Python programming language environment, through JupyterLab. As results, it was determined that a predominance of 57.27% of positive polarity, identifying that the relevant factors of student satisfaction with teaching performance, are related to the development of the teacher in the class sessions that contributes to the learning of the process control subject through the use of simulation tools such as simulink and tools linked to proportional integral derivative (PID) controllers; on the other hand, there is a percentage of negative polarity of 15.45% that belongs to the factors linked to the laboratory sessions in which graphic representation and block diagrams were used to explain the class session.

When virtual education was implemented in Peru, the limitations of teachers in technological management were evident. For this reason, the research seeks to analyze the perception of university satisfaction regarding the use of virtual teacher tools as part of teaching strategies, in order to improve virtual teaching-learning, achieving student motivation and facilitating this meaningful learning through the use of virtual tools. The method used according to the investigative approach is qualitative, according to its scope it is descriptive and correlational. During the development of the research, it was identified that the satisfaction regarding the use of virtual tools by the teacher is focused on the critical, constructive and positive attitude towards virtual tools and in the acquittal of students' questions regarding the use of virtual tools. On the other hand, the indicator that is related to low student satisfaction focuses on the low diversity of methodological strategies used for the development of virtual learning sessions. Likewise, the Chi-square test shows the significant relationship between the perception of the teacher's competences regarding the use of virtual tools and the perception of the quality of the teaching offered to students during distance education.