Research on corpus analysis based on big data

The big data of computer corpus contains a large amount of language and character materials, including words in various languages such as the Internet and books. It is very important to align data mining and then develop various language big models. This paper focuses on the corpus analysis and research based on big data, uses the BERT pre training model to upgrade the dimensions of tokens in the corpus, and carries out a series of processing operations on tokens. By combining deep learning and NLP techniques, we have successfully achieved in-depth analysis of large-scale corpora. We have elaborated on how to use the BERT model to upscale tokens in the corpus. Through the embedding layer of the BERT model, we map each token to a high-dimensional vector space, capturing the semantic relationships and contextual information between tokens. In addition, we also perform a series of processing operations on tokens, including removing stop words, extracting stems, and labeling part of speech. These processing operations help us further refine the corpus, improve the accuracy and efficiency of analysis. The key is to consider the differences in feature distribution among different corpora, and we perform specific processing on French corpora.