To extract topics from massive amounts of text, you can use topic modeling libraries in Python, such as gensim and scikit-learn. The following are the basic steps to use the gensim library to extract topics from massive texts: 1. Prepare the data: Convert the text data into the input format expected by gensim, which is bag-of-words representation or TF-IDF (word frequency- inverse document frequency) notation. 2. Training model: Use the LDA (Latent Dirichlet Allocation) model in gensim to train the topic model. 3. Evaluate the model: Evaluate the performance of the model by calculating indicators such as perplexity and topic coherence. 4. Apply the model: Input new text data into the trained model to obtain the topic distribution of the text. The following are the basic steps for extracting topics from massive amounts of text using the scikit-learn library: 1. Prepare the data: Convert the text data into the input format expected by scikit-learn, which is the TF-IDF representation. 2. Training model: Use the NMF (Non-negative Matrix Factorization) model in scikit-learn to train the topic model. 3. Evaluate the model: Evaluate the performance of the model by calculating indicators such as reconstruction error. 4. Apply the model: Input new text data into the trained model to obtain the topic distribution of the text. It should be noted that extracting topics from massive texts requires a lot of computing resources and time. Therefore, you can consider using distributed computing frameworks, such as Apache Spark and Dask, to accelerate calculations. Octopus Collector supports exporting data to CSV, Excel and other formats to facilitate further data processing and analysis in Python. Octopus has prepared a series of concise and easy-to-understand tutorials for users to help you quickly master collection techniques and easily handle data collection from various websites. Please go to the official website tutorials and help for more details.