Python uses Gensim text similarity calculation

In order to use Gensim to calculate text similarity in Python, it is necessary to first establish the corresponding environment and preparation work. 1. Install Python and Gensim: Firstly, ensure that Python has been installed. Then you can use pip to install the Gensim library. Open the command line and run the following command to install: pip install gensim 2. Prepare the dataset: For text similarity calculation, we can use some open-source datasets, such as the MovieLens dataset. The MovieLens dataset contains movie rating data and movie description data, making it very suitable for calculating text similarity. You can download it from the following website: https://grouplens.org/datasets/movielens/ Choose to download the 'database. gzip' file. 3. Decompress the dataset: After downloading, extract the file to a suitable location. In this example, we extract the dataset into a folder named 'movielens'. 4. Sample data: The MovieLens dataset provides rating data and movie description data. We will use movie description data to calculate text similarity. It is contained in a file called 'movies. csv' and has the following format: movieId,title,genres Among them, movieId is the unique identifier of the movie, title is the title of the movie, and genres is the type of movie. 5. Complete source code: The following is a complete example code for calculating text similarity using Gensim: python from gensim.models import Word2Vec from gensim.models.doc2vec import TaggedDocument import pandas as pd #Load Dataset data = pd.read_csv('movielens/movies.csv') #Using TaggedDocument to convert movie description data into a document list documents = [TaggedDocument(doc.split(), [i]) for i, doc in enumerate(data['title'])] #Building a Word2Vec model model = Word2Vec(documents, vector_size=100, window=5, min_count=1, workers=4) #Calculate the similarity between two documents similarity = model.wv.similarity('Toy Story (1995)', 'GoldenEye (1995)') print(f'Similarity between "Toy Story (1995)" and "GoldenEye (1995)": {similarity}') In this example, we first loaded the movie description dataset using the Pandas library. Then, we use TaggedDocument to convert the movie title into a list of documents, each with a unique identifier. Next, we constructed a word vector model using the Word2Vec model and calculated the similarity between two movie titles using the model's Wv.similarity method. This is a simple example where you can adjust model parameters and use different datasets according to your own needs. I hope it will be helpful to you!