Response Generation in Social Network With Topic and Emotion Constraints

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

Cognitive psychology · Cognitive science · Computer Science · Psychology · Text and Document Classification Technologies · Artificial Intelligence

• Insight in Sight

  Open Access•Apoorva Singh, Soumitra Ghosh et al.•IEEE Transactions on Computational…•2026

  Incluir item

• Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

  Open Access•Kyunghyun Cho, Bart van Merrienboer et al.•Proceedings of the 2014…•2014

  Incluir item

• Eliza—a computer program for the study of natural language communication between man and machine

  Open Access•Joseph Weizenbaum•Communications of the ACM•1966

  Incluir item

• Long Short-Term Memory

  Sepp Hochreiter, Jurgen Schmidhuber•Neural Computation•1997

  Incluir item

• Rlca

  Open Access•Yun Su, Haoran Bian et al.•IEEE Transactions on Computational…•2024

  Incluir item

• The cueing power of comments on social media

  Open Access•Anamaria Dutceac Segesten, Michael Bossetta et al.•Information Communication & Society•2022

  Incluir item

• Finding Structure in Time

  Open Access•Jeffrey L Elman•Cognitive Science•1990

  Incluir item