Python using Gensim text preprocessing

Environmental construction and preparation work: 1. Install Python: From the official Python website（ https://www.python.org/downloads/ ）Download the appropriate Python version and follow the instructions to install it. 2. Install Gensim: Open the command line terminal and execute the following command to install Gensim: pip install gensim 3. Download Dataset: In this example, we will use the 'Text8' dataset provided by Gensim. You can download the dataset from the following website: http://mattmahoney.net/dc/text8.zip After downloading, extract it and place the 'text8' file in the project directory. Dependent class libraries: In this example, we will use the following class libraries: -Gensim: used for text preprocessing and Vector space modeling. -Nltk: used to build the Bag-of-words model model and filter Stop word. Sample data: We will use the Text8 dataset for text preprocessing. The Text8 dataset is a preprocessed English Wikipedia corpus that has been converted to a string of words separated only by spaces. The example code is as follows: python from gensim import corpora from nltk.corpus import stopwords from gensim.models import Word2Vec import string import nltk #Download Stop word nltk.download('stopwords') def preprocess_text(text): #Convert text to lowercase text = text.lower() #Remove Punctuation text = text.translate(str.maketrans('', '', string.punctuation)) #Remove Stop word stop_words = set(stopwords.words('english')) text = ' '.join(word for word in text.split() if word not in stop_words) return text def main(): #Read Dataset with open('text8') as f: text = f.read() #Preprocessing Text processed_text = preprocess_text(text) #Convert text into a list of sentences sentences = processed_text.split('.') #Convert sentence list to word list word_lists = [sentence.split() for sentence in sentences] #Create Bag-of-words model model dictionary = corpora.Dictionary(word_lists) #Create a corpus to convert text into vector representations corpus = [dictionary.doc2bow(word_list) for word_list in word_lists] #Train Word2Vec model model = Word2Vec(word_lists, min_count=1) if __name__ == '__main__': main() Please note that this example only includes the basic steps of text preprocessing and Word2Vec model training. You can choose and modify according to your own needs.