How to Cluster Documents Using Word2Vec and K-means

python
nlp
ml
Author
Affiliation

Dylan Castillo

Iwana Labs

Published

January 18, 2021

Modified

November 20, 2024

There are more sentiment analysis tutorials online than people doing sentiment analysis in their day jobs. Don’t get me wrong. I’m not saying those tutorials aren’t useful. I just want to highlight that supervised learning receives much more attention than any other Natural Language Processing (NLP) method.

Oddly enough, there’s a big chance that most of the text data you’ll use in your next projects won’t have ground truth labels. So supervised learning might not be a solution you can immediately apply to your data problems.

What can you do then? Use unsupervised learning algorithms.

In this tutorial, you’ll learn to apply unsupervised learning to generate value from your text data. You’ll cluster documents by training a word embedding (Word2Vec) and applying the K-means algorithm.

Please be aware that the next sections focus on practical manners. You won’t find much theory in them besides brief definitions of relevant ideas.

To make the most of this tutorial, you should be familiar with these topics:

Let’s get to it!

How to Cluster Documents

You can think of the process of clustering documents in three steps:

  1. Cleaning and tokenizing data usually involves lowercasing text, removing non-alphanumeric characters, or stemming words.
  2. Generating vector representations of the documents concerns the mapping of documents from words into numerical vectors—some common ways of doing this include using bag-of-words models or word embeddings.
  3. Applying a clustering algorithm on the document vectors requires selecting and applying a clustering algorithm to find the best possible groups using the document vectors. Some frequently used algorithms include K-means, DBSCAN, or Hierarchical Clustering.

That’s it! Now, you’ll see how that looks in practice.

Sample Project: Clustering News Articles

In this section, you’ll learn how to cluster documents by working through a small project. You’ll group news articles into categories using a dataset published by Szymon Janowski.

Set Up Your Local Environment

To follow along with the tutorial examples, you’ll need to download the data and install a few libraries. You can do it by following these steps:

  1. Clone the nlp-snippets repository locally.
  2. Create a new virtual environment using venv or conda.
  3. Activate your new virtual environment.
  4. Install the required libraries.
  5. Start a Jupyter notebook.

If you’re using venv, then you need to run these commands:

git clone https://github.com/dylanjcastillo/nlp-snippets.git
python3 -m venv venv
source venv/bin/activate
pip install -r requirements
jupyter notebook

If you’re using conda, then you need to run these commands:

git clone https://github.com/dylanjcastillo/nlp-snippets.git
conda create --name venv
conda activate venv
pip install -r requirements
jupyter notebook

Next, open Jupyter Notebook. Then, create a new notebook in the root folder and set its name to clustering_word2vec.ipynb.

By now, your project structure should look like this:

nlp-snippets/
│
├── clustering/
│
├── data/
│
├── ds_utils/
│
├── preprocessing/
│
├── venv/ # (If you're using venv)
│
├── clustering_word2vec.ipynb
├── LICENSE
├── README.md
└── requirements.txt

This is your project’s structure. It includes these directories and files:

  • clustering/: Examples of clustering text data using bag-of-words, training a word2vec model, and using a pretrained fastText embeddings.
  • data/: Data used for the clustering examples.
  • ds_utils/: Common utility functions used in the sample notebooks in the repository.
  • preprocessing/: Frequently used code snippets for preprocessing text.
  • venv/: If you used venv, then this directory will contain the files related to your virtual environment.
  • requirements.txt: Libraries used in the examples provided.
  • README and License: Information about the repository and its license.

For now, you’ll use the notebook you created (clustering_word2vec.ipynb) and the news dataset in data/. The notebooks in clustering/ and preprocessing/ include additional code snippets that might be useful for NLP tasks. You can review those on your own.

In the next section, you’ll create the whole pipeline from scratch. If you’d like to download the full and cleaner version of the code in the examples, go to the NLP Snippets repository.

That’s it for setup! Next, you’ll define your imports.

Import the Required Libraries

Once you finish setting up your local environment, it’s time to start writing code in your notebook. Open clustering_word2vec.ipynb, and copy the following code in the first cell:

import os
import random
import re
import string

import nltk
import numpy as np
import pandas as pd

from gensim.models import Word2Vec

from nltk import word_tokenize
from nltk.corpus import stopwords

from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics import silhouette_samples, silhouette_score

nltk.download("stopwords")
nltk.download("punkt")

SEED = 42
random.seed(SEED)
os.environ["PYTHONHASHSEED"] = str(SEED)
np.random.seed(SEED)

These are the libraries you need for the sample project. Here’s what you do with each of them:

  • os and random help you define a random seed to make the code deterministically reproducible.
  • re and string provide you with easy ways to clean the data.
  • pandashelps you read the data.
  • numpyprovides you with linear algebra utilities you’ll use to evaluate results. Also, it’s used for setting a random seed to make the code deterministically reproducible.
  • gensim makes it easy for you to train a word embedding from scratch using the Word2Vec class.
  • nltkaids you in cleaning and tokenizing data through the word_tokenize method and the stopword list.
  • sklearngives you an easy interface to the clustering model, MiniBatchKMeans, and the metrics to evaluate the quality of its results, silhouette_samples and silhouette_score.

In addition to importing the libraries, you download English stopwords using nltk.download("stopwords"), you define SEED and set it as a random seed using numpy, random, and the PYTHONHASHSEED environment variable. This last step makes sure your code is reproducible across systems.

Run this cell and make sure you don’t get any errors. In the next section, you’ll prepare your text data.

Clean and Tokenize Data

After you import the required libraries, you need to read and preprocess the data you’ll use in your clustering algorithm. The preprocessing consists of cleaning and tokenizing the data. To do that, copy the following function in a new cell in your notebook:

def clean_text(text, tokenizer, stopwords):
    """Pre-process text and generate tokens

    Args:
        text: Text to tokenize.

    Returns:
        Tokenized text.
    """
    text = str(text).lower()  # Lowercase words
    text = re.sub(r"\[(.*?)\]", "", text)  # Remove [+XYZ chars] in content
    text = re.sub(r"\s+", " ", text)  # Remove multiple spaces in content
    text = re.sub(r"\w+…|…", "", text)  # Remove ellipsis (and last word)
    text = re.sub(r"(?<=\w)-(?=\w)", " ", text)  # Replace dash between words
    text = re.sub(
        f"[{re.escape(string.punctuation)}]", "", text
    )  # Remove punctuation

    tokens = tokenizer(text)  # Get tokens from text
    tokens = [t for t in tokens if not t in stopwords]  # Remove stopwords
    tokens = ["" if t.isdigit() else t for t in tokens]  # Remove digits
    tokens = [t for t in tokens if len(t) > 1]  # Remove short tokens
    return tokens

This code cleans and tokenizes a text input, using a predefined tokenizer and a list of stopwords. It helps you perform these operations:

  1. Line 10: Transform the input into a string and lowercase it.
  2. Line 11: Remove substrings like “[+300 chars]” I found while reviewing the data.
  3. Line 12: Remove multiple spaces, tabs, and line breaks.
  4. Line 13: Remove ellipsis characters.
  5. Lines 14-17: Replace dashes between words with a space and remove punctuation.
  6. Lines 19-20: Tokenize text and remove tokens using a list of stop words.
  7. Lines 21-22: Remove digits and tokens whose length is too short.

Then, in the next cell, copy the following code to read the data and apply that function to the text columns:

custom_stopwords = set(stopwords.words("english") + ["news", "new", "top"])
text_columns = ["title", "description", "content"]

df_raw = pd.read_csv("data/news_data.csv")
df = df_raw.copy()
df["content"] = df["content"].fillna("")

for col in text_columns:
    df[col] = df[col].astype(str)

# Create text column based on title, description, and content
df["text"] = df[text_columns].apply(lambda x: " | ".join(x), axis=1)
df["tokens"] = df["text"].map(lambda x: clean_text(x, word_tokenize, custom_stopwords))

# Remove duplicated after preprocessing
_, idx = np.unique(df["tokens"], return_index=True)
df = df.iloc[idx, :]

# Remove empty values and keep relevant columns
df = df.loc[df.tokens.map(lambda x: len(x) > 0), ["text", "tokens"]]

docs = df["text"].values
tokenized_docs = df["tokens"].values

print(f"Original dataframe: {df_raw.shape}")
print(f"Pre-processed dataframe: {df.shape}")

This is how you read and preprocess the data. This code applies the cleaning function you defined earlier, removes duplicates and nulls, and drops irrelevant columns.

You apply these steps to a new data frame (df). It contains a column with the raw documents called text and another one with the preprocessed documents called tokens. You save the values of those columns into two variables, docs and tokenized_docs, to use in the next code snippets.

If you execute the two cells you defined, then you should get the following output:

Original dataframe: (10437, 15)
Pre-processed dataframe: (9882, 2)

Next, you’ll create document vectors using Word2Vec.

Generate Document Vectors

After you’ve cleaned and tokenized the text, you’ll use the documents’ tokens to create vectors using Word2Vec. This process consists of two steps:

  1. Train a Word2Vec model using the tokens you generated earlier. Alternatively, you could load a pre-trained Word2Vec model (I’ll also show you how to do it).
  2. Generate a vector per document based on its individual word vectors.

In this section, you’ll go through these steps.

Train Word2Vec Model

The following code will help you train a Word2Vec model. Copy it into a new cell in your notebook:

model = Word2Vec(sentences=tokenized_docs, vector_size=100, workers=1, seed=SEED)

You use this code to train a Word2Vec model based on your tokenized documents. For this example, you specified the following parameters in the Word2Vec class:

  • sentences expects a list of lists with the tokenized documents.
  • vector_size defines the size of the word vectors. In this case, you set it to 100.
  • workers defines how many cores you use for training. I set it to 1 to make sure the code is deterministically reproducible.
  • seed sets the seed for random number generation. It’s set to the constant SEED you defined in the first cell.

There are other parameters you can tune when training the Word2Vec model. See gensim’s documentation if you’d like to learn more about them.

Note: In many cases, you might want to use a pre-trained model instead of training one yourself. If that’s the case, gensim provides you with an easy way to access some of the most popular pre-trained word embeddings.

You can load a pre-trained Word2Vec model as follows:

wv = api.load('word2vec-google-news-300')

One last thing, if you’re following this tutorial and decide to use a pre-trained model, you’ll need to replace model.wv by wv in the code snippets from here on. Otherwise, you’ll get an error.

Next, run the cell you just created in your notebook. It might take a couple of minutes. After it’s done, you can validate that the results make sense by plotting the vectors or reviewing the similarity results for relevant words. You can do the latter by copying and running this code in a cell in your notebook:

model.wv.most_similar("trump")

If you run this code, then you’ll get this output:

[('trumps', 0.988541841506958),
 ('president', 0.9746493697166443),
 ('donald', 0.9274922013282776),
 ('ivanka', 0.9203903079032898),
 ('impeachment', 0.9195784330368042),
 ('pences', 0.9152231812477112),
 ('avlon', 0.9148306846618652),
 ('biden', 0.9146010279655457),
 ('breitbart', 0.9144087433815002),
 ('vice', 0.9067237973213196)]

That’s it! You’ve trained your Word2Vec model, now, you’ll use it to generate document vectors.

Create Document Vectors from Word Embedding

Now you’ll generate document vectors using the Word2Vec model you trained. The idea is straightforward. From the Word2Vec model, you’ll get numerical vectors per word in a document, so you need to find a way of generating a single vector out of them.

For short texts, a common approach is to use the average of the vectors. There’s no clear consensus on what will work well for longer texts. Though, using a weighted average of the vectors might help.

The following code will help you create a vector per document by averaging its word vectors. Create a new cell in your notebook and copy this code there:

def vectorize(list_of_docs, model):
    """Generate vectors for list of documents using a Word Embedding

    Args:
        list_of_docs: List of documents
        model: Gensim's Word Embedding

    Returns:
        List of document vectors
    """
    features = []

    for tokens in list_of_docs:
        zero_vector = np.zeros(model.vector_size)
        vectors = []
        for token in tokens:
            if token in model.wv:
                try:
                    vectors.append(model.wv[token])
                except KeyError:
                    continue
        if vectors:
            vectors = np.asarray(vectors)
            avg_vec = vectors.mean(axis=0)
            features.append(avg_vec)
        else:
            features.append(zero_vector)
    return features

vectorized_docs = vectorize(tokenized_docs, model=model)
len(vectorized_docs), len(vectorized_docs[0])

This code will get all the word vectors of each document and average them to generate a vector per each document. Here’s what’s happening there:

  1. You define the vectorize function that takes a list of documents and a gensim model as input, and generates a feature vector per document as output.
  2. You apply the function to the documents’ tokens in tokenized_doc, using the Word2Vec model you trained earlier.
  3. You print the length of the list of documents and the size of the generated vectors.

Next, you’ll cluster the documents using Mini-batches K-means.

Cluster Documents Using (Mini-batches) K-means

To cluster the documents, you’ll use the Mini-batches K-means algorithm. This K-means variant uses random input data samples to reduce the time required during training. The upside is that it shares the same objective function with the original algorithm, so, in practice, the results are just a bit worse than K-means.

In the code snippet below, you can see the function you’ll use to create the clusters using Mini-batches K-means. Create a new cell in your notebook, and copy the following code there:

def mbkmeans_clusters(
    X,
    k,
    mb,
    print_silhouette_values,
):
    """Generate clusters and print Silhouette metrics using MBKmeans

    Args:
        X: Matrix of features.
        k: Number of clusters.
        mb: Size of mini-batches.
        print_silhouette_values: Print silhouette values per cluster.

    Returns:
        Trained clustering model and labels based on X.
    """
    km = MiniBatchKMeans(n_clusters=k, batch_size=mb).fit(X)
    print(f"For n_clusters = {k}")
    print(f"Silhouette coefficient: {silhouette_score(X, km.labels_):0.2f}")
    print(f"Inertia:{km.inertia_}")

    if print_silhouette_values:
        sample_silhouette_values = silhouette_samples(X, km.labels_)
        print(f"Silhouette values:")
        silhouette_values = []
        for i in range(k):
            cluster_silhouette_values = sample_silhouette_values[km.labels_ == i]
            silhouette_values.append(
                (
                    i,
                    cluster_silhouette_values.shape[0],
                    cluster_silhouette_values.mean(),
                    cluster_silhouette_values.min(),
                    cluster_silhouette_values.max(),
                )
            )
        silhouette_values = sorted(
            silhouette_values, key=lambda tup: tup[2], reverse=True
        )
        for s in silhouette_values:
            print(
                f"    Cluster {s[0]}: Size:{s[1]} | Avg:{s[2]:.2f} | Min:{s[3]:.2f} | Max: {s[4]:.2f}"
            )
    return km, km.labels_

This function creates the clusters using the Mini-batches K-means algorithm. It takes the following arguments:

  • **X**: Matrix of features. In this case, it’s your vectorized documents.
  • **k**:Number of clusters you’d like to create.
  • **mb**: Size of mini-batches.
  • **print_silhouette_values**: Defines if the Silhouette Coefficient is printed for each cluster. If you haven’t heard about this coefficient, don’t worry, you’ll learn about it in a bit!

mbkmeans_cluster takes these arguments and returns the fitted clustering model and the labels for each document.

Run the cell where you copied the function. Next, you’ll apply this function to your vectorized documents.

Definition of Clusters

Now, you need to execute mbkmean_clusters providing it with the vectorized documents and the number of clusters. You’ll print the Silhouette Coefficients per cluster to review the quality of your clusters.

Create a new cell and copy this code there:

clustering, cluster_labels = mbkmeans_clusters(
    X=vectorized_docs,
    k=50,
    mb=500,
    print_silhouette_values=True,
)
df_clusters = pd.DataFrame({
    "text": docs,
    "tokens": [" ".join(text) for text in tokenized_docs],
    "cluster": cluster_labels
})

This code will fit the clustering model, print the Silhouette Coefficient per cluster, and return the fitted model and the labels per cluster. It’ll also create a data frame you can use to review the results.

There are a few things to consider when setting the input arguments:

  • print_silhouette_values is straightforward. In this case, you set it to True to print the evaluation metric per cluster. This will help you review the results.
  • mb depends on the size of your dataset. You need to ensure that it is not too small to avoid a significant impact on the quality of results and not too big to avoid making the execution too slow. In this case, you set it to 500 observations.
  • k is trickier. In general, it involves a mix of qualitative analysis and quantitative metrics. After a few experiments on my side, I found that 50 seemed to work well. But that is more or less arbitrary.

You could use metrics like the Silhouette Coefficient for the quantitative evaluation of the number of clusters. This coefficient is an evaluation metric frequently used in problems where ground truth labels are unknown. It’s calculated using the mean intra-cluster distance and the mean nearest-cluster distance and goes from -1 to 1. Well-defined clusters result in positive values of this coefficient, while incorrect clusters will result in negative values. If you’d like to learn more about it, look at scikit-learn’s documentation.

The qualitative part generally requires you to have domain knowledge of the subject matter so you can sense-check your clustering algorithm’s results. In the next section, I’ll show you two approaches you can use to check your results qualitatively.

After executing the cell you just created, the output should look like this:

For n_clusters = 50
Silhouette coefficient: 0.11
Inertia:3568.342791047967
Silhouette values:
    Cluster 29: Size:50 | Avg:0.39 | Min:0.01 | Max: 0.59
    Cluster 35: Size:30 | Avg:0.34 | Min:0.05 | Max: 0.54
    Cluster 37: Size:58 | Avg:0.32 | Min:0.09 | Max: 0.51
    Cluster 39: Size:81 | Avg:0.31 | Min:-0.05 | Max: 0.52
    Cluster 27: Size:63 | Avg:0.28 | Min:0.02 | Max: 0.46
    Cluster 6: Size:101 | Avg:0.27 | Min:0.02 | Max: 0.46
    Cluster 24: Size:120 | Avg:0.26 | Min:-0.04 | Max: 0.46
    Cluster 49: Size:65 | Avg:0.26 | Min:-0.03 | Max: 0.47
    Cluster 47: Size:53 | Avg:0.23 | Min:0.01 | Max: 0.45
    Cluster 22: Size:78 | Avg:0.22 | Min:-0.01 | Max: 0.43
    Cluster 45: Size:38 | Avg:0.21 | Min:-0.07 | Max: 0.41
...

This is the output of your clustering algorithm. The sizes and Silhouette Coefficients per cluster are the most relevant metrics. The clusters are printed by the value of the Silhouette coefficient in descending order. A higher score means denser – and thus better – clusters. In this case, you can see that clusters 29, 35, and 37 seem to be the top ones.

Next, you’ll learn how to check what’s in each cluster.

Qualitative Review of Clusters

There are a few ways you can qualitatively analyze the results. During the earlier sections, our approach resulted in vector representations of tokens and documents, and vectors of the clusters’ centroids. You can find the most representative tokens and documents to analyze the results by looking for the vectors closest to the clusters’ centroids.

Here’s how you obtain the most representative tokens per cluster:

print("Most representative terms per cluster (based on centroids):")
for i in range(50):
    tokens_per_cluster = ""
    most_representative = model.wv.most_similar(positive=[clustering.cluster_centers_[i]], topn=5)
    for t in most_representative:
        tokens_per_cluster += f"{t[0]} "
    print(f"Cluster {i}: {tokens_per_cluster}")

For the top clusters we identified earlier – 29, 35, and 37 – these are the results:

Cluster 29: noaa sharpie claim assertions forecasters
Cluster 35: eye lilinow path halts projected
Cluster 37: cnnpolitics complaint clinton pences whistleblower

Next, we can do the same analysis with documents instead of tokens. This is how you find the most representative documents for cluster 29:

test_cluster = 29
most_representative_docs = np.argsort(
    np.linalg.norm(vectorized_docs - clustering.cluster_centers_[test_cluster], axis=1)
)
for d in most_representative_docs[:3]:
    print(docs[d])
    print("-------------")

And these are the 3 most representative documents in that cluster:

Dorian, Comey and Debra Messing: What Trump tweeted on Labor Day weekend | President Donald Trump axed his visit to Poland over the weekend to monitor Hurricane Dorian from Camp David with emergency management staff, but if the President's more than 120 tweets are any indication, he had more than just the storm on his mind. | Washington (CNN)President Donald Trump axed his visit to Poland over the weekend to monitor Hurricane Dorian from Camp David with emergency management staff, but if the President's more than 120 tweets are any indication, he had more than just the storm on hi… [+3027 chars]
-------------
Ross Must Resign If Report He Threatened NOAA Officials Is True: Democrat | As President Donald Trump claimed Hurricane Dorian could hit Alabama, the National Weather Service tweeted to correct the rumors. | Commerce Secretary Wilbur Ross is facing calls to resign over a report alleging that he threatened to fire top officials at NOAA for a tweet disputing President Donald Trump's claim that Hurricane Dorian would hit Alabama.
"If that story is true, and I don't… [+3828 chars]
-------------
Federal weather workers are furious at the NOAA's 'utterly disgusting' statement defending Trump's claim Hurricane Dorian would hit Alabama | Federal weather workers have reacted furiously to the National Oceanic and Atmospheric Administration's (NOAA) defence of US President Donald Trump's repeated assertions that Hurricane Dorian was set to hit Alabama. "Never ever before has their management thr… | Federal weather workers have reacted furiously to the National Oceanic and Atmospheric Administration's (NOAA) defence of US President Donald Trump's repeated assertions that Hurricane Dorian was set to hit Alabama, saying they have been "thrown under the bus… [+3510 chars]

Most of the results seem to be related to a dispute between Donald Trump and the National Oceanic and Atmospheric Agency (NOAA). It was a famous controversy that people referred to as Sharpiegate.

You could also explore other approaches like generating word frequencies per cluster or reviewing random samples of documents per cluster.

Other Approaches

There are other approaches you could take to cluster text data like:

Conclusion

Way to go! You just learned how to cluster documents using Word2Vec. You went through an end-to-end project, where you learned all the steps required for clustering a corpus of text.

You learned how to:

  • Preprocess data to use with a Word2Vec model
  • Train a Word2Vec model
  • Use quantitative metrics, such as the Silhouette score to evaluate the quality of your clusters.
  • Find the most representative tokens and documents in your clusters

I hope you find this tutorial useful. Shoot me a message if you have any questions!

Citation

BibTeX citation:
@online{castillo2021,
  author = {Castillo, Dylan},
  title = {How to {Cluster} {Documents} {Using} {Word2Vec} and
    {K-means}},
  date = {2021-01-18},
  url = {https://dylancastillo.co/posts/nlp-snippets-cluster-documents-using-word2vec.html},
  langid = {en}
}
For attribution, please cite this work as:
Castillo, Dylan. 2021. “How to Cluster Documents Using Word2Vec and K-Means.” January 18, 2021. https://dylancastillo.co/posts/nlp-snippets-cluster-documents-using-word2vec.html.