Research on the Cultural Media Content Generation Model Driven by Natural Language Processing Technology

This study focuses on the application of natural language processing technology in the field of cultural media content generation, and is committed to building an efficient and targeted content generation model. A cultural media content generation algorithm based on sentiment fusion and semantic expansion (ESACG) is proposed. With the help of precise sentiment analysis technology, the algorithm converts the sentiment tendency of the input text into the key elements to guide content generation, and through a unique semantic expansion method, uses knowledge graphs and pre-trained language models to mine rich semantic information to enrich the content. The experiment uses a large-scale cultural media field text dataset collected from multiple channels, and compares it with mainstream models such as traditional seq2seq models and fine-tuning models based on GPT-3. The experimental results show that in terms of the BLEU value evaluation index, the model reaches 0.68, which is 25% higher than the traditional seq2seq model, the ROUGE-1 index reaches 0.82, and the ROUGE-2 index reaches 0.60, which are significantly better than the comparison model. In terms of the emotional fit index for the cultural media field, this model reaches 85%, and the cultural element richness index is 30% higher than the comparison model.