Python uses Gensim theme modeling to extract several themes from a large number of articles

Environmental construction and preparation work: 1. Install Python: Ensure that the Python interpreter has been installed. 2. Install Gensim library: Use the following command on the command line to install Gensim library: pip install Gensim. 3. Download dataset: You can use the dataset provided by Gensim or download the corpus required for topic modeling from other sources. Dependent class library: Gensim Dataset download website: Gensim provides some sample datasets that can be directly downloaded and used. Please refer to Gensim's official documentation for details. Sample data: Taking Gensim's 20 newsgroup datasets as an example, this dataset contains 18846 news articles from 20 different themes. The following is a complete example of Gensim based theme modeling: python from gensim import corpora from gensim.models import LdaModel from gensim.test.utils import datapath #Load Dataset data_path = datapath('20newsgroups') corpus = corpora.BleiCorpus(data_path) #Building a Bag-of-words model model dictionary = corpus.dictionary #Train LDA model num_topics = 10 lda_model = LdaModel(corpus, num_topics=num_topics, id2word=dictionary) #Print keywords for each topic topics = lda_model.print_topics(num_topics) for topic in topics: print(topic) The above code first loads 20 newsgroup datasets and converts them into Gensim corpus format using 'corpora. BleiCorpus'. Then the Bag-of-words model model is built through 'cores. dictionary'. Next, use 'LdaModel' to train the corpus and specify a number of topics as 10. Finally, through 'lda'_ Model. print_ The topics' print out the keywords for each topic. Note: Before running the code, you need to download and extract 20 newsgroup datasets. You can find the download address and decompression method for this dataset in Gensim's GitHub repository.