Daily, people have access to various multimedia content, that can change their emotional state and it can cause an increase in their heart rate. These emotional changes are troubling in the elderly people. Thus, this work aims to classify emotions by machine learning and monitor the emotions of the elderly through the measurement of heart rate, using portable sensors of low cost and easily to use. For this, a framework is implemented, which presents to the user messages of attention and danger according to emotion and heart rate when video contents are presented to the elderly people. Also, the audio transcription of the videos are performed and the sentiment analysis is performed. Thus, when detecting a sudden increase in heart rate and the video presents a negative sentiment polarity, then the proposed solution presents a video of calm or happy content, according to the user's pre-registered preference. The experimental results showed that the classification system performed by a deep learning model obtained values of 97% of accuracy for classifying the disgust emotion and the heart rate level decreased with the proposed framework. Additionally, 93.75% of the users considered the system efficient.