Extracting knowledge from digital social platforms is a new trend in software requirements engineering. The feedback generated by users in these environments is a very valuable source of needs, demands, dissatisfactions, and judgments about the software applications they use. The effectively processing of this volume of information for predicting the software product evolution is a great challenge, which can start from determining the most relevant contents, to the automatic elicitation of new requirements for that product. This paper proposes a method for generating software requirements from user feedback. This solution combines Deep Learning models for determining the relevant information, with an LLM (Large Language Model) to generate functional and non-functional requirements about a software and specific aspects from the feedback. This solution was evaluated using datasets of opinions from 4 different applications. The results were very promising, because they showed improvements over other reported solutions, and demonstrated that with a proper conception of information filtering and specification of interests through the prompt it is possible to generate good quality requirements using LLMs from an informal information source.