Response generation is the task of automatically generating human-like content based on the provided context. One of its prominent applications is to simulate realistic response content for social network posts. In the digital age, social network platforms play a vital role in information exchange and social interaction. This study focuses on response generation techniques for the platform of public opinion evolution simulation that simulate realistic response content, enabling a deeper understanding of the emotional expressions of network users. Recent advancements in deep learning techniques, particularly the sequence-to-sequence (Seq2Seq) model, have shown promise in the response generation field. However, we still face two challenges: content variety, topic and emotion relevancy. To this end, we propose the EmoTG-ETRS model which comprises three parts. The first is a response generation module based on Transformer architecture. Then, an auxiliary emotion improvement module is incorporated to enhance the emotional expressiveness of the response candidates. Finally, a reverse selection module, which combines maximum mutual information (MMI) evaluation, emotional expression evaluation, and topic consistency evaluation, is devised to select the highest-scoring response. Extensive experiments have been conducted to evaluate the effectiveness of the proposed model and the results demonstrate that the EmoTG-ETRS model improves the quality of produced replies in terms of topic consistency and emotional accuracy rate when compared with the SOTA research works.