How to use Gensim for text clustering?

Gensim is a popular machine learning library for text clustering. Here are the general steps to use Gensim for text clustering:

1. Load and preprocess the text data.
2. Create document vectors using Gensim’s Doc2Vec or Word2Vec model.
3. Apply clustering algorithms such as K-Means or hierarchical clustering to group similar documents together.

Here’s an example of using Gensim for text clustering using K-Means clustering algorithm:

First, install Gensim and any other required libraries (if not already installed):

pip install gensim
pip install numpy
pip install scipy
pip install sklearn

Next, load and preprocess the text data. Here is an example of how to use Gensim to preprocess text data:

import gensim
from gensim.utils import simple_preprocess
from gensim.parsing.preprocessing import STOPWORDS

# load and preprocess text data
data = ['text document 1', 'text document 2', 'text document 3']
processed_data = [simple_preprocess(doc) for doc in data]

Then, create document vectors using Gensim’s Doc2Vec or Word2Vec model:

# create Doc2Vec model
model = gensim.models.doc2vec.Doc2Vec(processed_data, vector_size=100, window=5, min_count=1, workers=4)

# infer document vectors
vectors = [model.infer_vector(doc) for doc in processed_data]

Finally, apply K-Means clustering algorithm to group similar documents together:

from sklearn.cluster import KMeans

# apply K-Means clustering
num_clusters = 3 # number of clusters to form
kmeans_model = KMeans(n_clusters=num_clusters, init='k-means++', max_iter=100) 
kmeans_model.fit(vectors)

# print the clusters
for i in range(num_clusters):
    print("Cluster ", i+1)
    for j in range(len(data)):
        if kmeans_model.labels_[j] == i:
            print(data[j])

This will print out the clusters and the documents in each cluster according to the K-Means algorithm.

Note that the exact steps may vary depending on the specific use case and the characteristics of the text data.