A Comparison of Chinese-English News Metaphors for Corpus Analysis in the Context of Big Data Technology

In this paper, firstly, the Chinese-English news corpus is constructed using crawler technology, and a distributed retrieval system is designed to search the Chinese-English data in the corpus. Secondly, the potential semantic analysis algorithm is improved to extract semantic information from the Chinese-English corpus and realize the semantic search data source. Finally, by designing the experimental environment as well as the samples of the Chinese-English news corpus, the performance of the corpus as well as the distribution retrieval performance are analyzed, and the comparison of Chinese-English news metaphors is explored. The results show that the system’s word-level accuracy is 96.32% when the training corpus size reaches 100,000 word-level, the F-value is 96.08, and the latency of 1Mb/s is higher than the latency of 3Mb/s and the latency time of 5M/s, and the corresponding latency is always stabilized within 1.0. The proportion of conceptual metaphors in Chinese news is above 0.5, while in English news, the proportion is mostly around 0.3. Most of the Chinese news is conceptual metaphors, while most English news is interpersonal metaphors.