Text clustering has swiftly emerged as a cornerstone in data-driven decision-making across industries. But what exactly is text clustering, and how can it transform the way businesses operate? How does it convert unstructured text into actionable insights? What are the core steps involved in text clustering, and how are they interlinked? What algorithms are pivotal in implementing text clustering effectively? In this blog, we will unravel these questions, diving deep into the systematic steps of text clustering, its underlying algorithms, and real-world examples that bring this technique to life. Whether you’re a product manager seeking to leverage data analytics or a data scientist curious to learn key steps of text clustering with the help of examples, this blog will shed light on the vital role text clustering plays in extracting meaning from vast text data/corpus.
Text clustering is a technique used to group documents into clusters so that documents within the same cluster are more similar to each other than to those in other clusters. It’s a common approach in natural language processing and machine learning to uncover hidden structures in a dataset, categorize documents, and enable information retrieval. Let’s break down the process into three crucial stages:
Raw text data is unstructured and cannot be directly fed into clustering algorithms. The challenge lies in converting the text into a numerical format that represents the semantic meaning of the text. The solution is embedding the documents. This stage involves converting the text documents into numerical representations/vectors that capture the essence of the text. Consider a collection of news articles. To group them by topics, the articles must first be transformed into numerical vectors. In other words, we need to embed news articles into a numerical representation.
The following are different methods used for creating document embedding.
The following code represents Python code example for creating text embeddings as a first step in doing text clustering to fine the topics. The example is worked with this file namely customer_complaints_1.csv. The text embeddings have been created using TF-IDF mentioned above.
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
# Load the dataset (Google Colab)
df = pd.read_csv("/content/customer_complaints_1.csv")
# Initialize a TF-IDF vectorizer
tfidf_vectorizer = TfidfVectorizer(max_df=0.85, max_features=1000, stop_words='english')
# Fit and transform the complaints text to create embeddings
tfidf_matrix = tfidf_vectorizer.fit_transform(df['text'])
tfidf_matrix.shape
The TF-IDF embeddings have been created with a shape of (19, 751). This means we have 19 documents (complaints) and 751 features (terms) after filtering with our TF-IDF parameters.
The document embeddings (numerical vectors) obtained from the above step might be very high-dimensional, leading to computational complexity and the curse of dimensionality. The solution is reducing dimensionality. This step aims to reduce the dimensionality of the document vectors while preserving essential information.
The following are some of the methods which can be used for dimensionality reduction:
In the code below, the dimensionality is reduced using PCA. Since we have 19 documents, let’s aim to reduce our features to a manageable number, say 2, for visualization purposes.
from sklearn.decomposition import PCA
# Initialize PCA and reduce dimensionality to 2 components
pca = PCA(n_components=2)
reduced_tfidf = pca.fit_transform(tfidf_matrix.toarray())
reduced_tfidf.shape
The final challenge is to group the documents in such a way that similar documents are in the same cluster. The solution is clustering the embeddings. This stage groups the reduced-dimensional vectors into clusters based on similarity.
The following are some of the methods which can be used for clustering the embeddings:
In this blog, we will cluster the embeddings using k-means. Before clustering the embeddings, an important consideration is determining the optimal number of clusters (i.e., the value of k). One common method to determine k is the Elbow method, where we plot the sum of squared distances for different values of k and look for the “elbow” point where the reduction in variance slows down. The following code helps you determine the optimal number of clusters using Elbow method. We will work with reduced dimension in previous step.
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# Determine the optimal number of clusters using the Elbow method
wcss = [] # within-cluster sum of squares
cluster_range = range(1, 10) # test up to 10 clusters
for k in cluster_range:
kmeans = KMeans(n_clusters=k, random_state=42)
kmeans.fit(reduced_tfidf)
wcss.append(kmeans.inertia_)
# Plot the Elbow method
plt.figure(figsize=(10, 6))
plt.plot(cluster_range, wcss, marker='o', linestyle='--')
plt.xlabel('Number of Clusters')
plt.ylabel('Within-Cluster Sum of Squares')
plt.title('Elbow Method for Optimal Number of Clusters')
plt.grid(True)
plt.show()
The “elbow” in the plot indicates the optimal number of clusters where the reduction in variance starts to slow down. In this case, it seems that around k = 3 or k = 4 is where the “elbow” starts to form. We will work with k=3 in this example.
We will use k=3 for clustering the text using K-Means algorithm. Here is the Python code example.
# Perform KMeans clustering with 3 clusters
kmeans = KMeans(n_clusters=3, random_state=42)
clusters = kmeans.fit_predict(reduced_tfidf)
# Plot the clusters
plt.figure(figsize=(10, 6))
plt.scatter(reduced_tfidf[:, 0], reduced_tfidf[:, 1], c=clusters, cmap='rainbow')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=200, c='black', marker='X', label='Centroids')
plt.xlabel('PCA Component 1')
plt.ylabel('PCA Component 2')
plt.title('Clusters of Customer Complaints')
plt.legend()
plt.grid(True)
plt.show()
This is how the clusters will look like.
Below are some of the real-world examples of use cases where text clustering can prove to be beneficial:
Businesses often struggle with identifying common customer complaints, leading to repetitive and inefficient resolutions. Text clustering analyzes customer support interactions, groups similar queries or complaints, and highlights recurring issues, using techniques like TF-IDF with K-Means clustering. By identifying patterns, it helps in proactively addressing common problems, enhancing customer satisfaction, and providing quicker solutions.
Creating content that aligns with audience interests can be challenging, affecting SEO performance. Clustering website content to find topics that resonate with the audience is the solution, with tools like Word2Vec and hierarchical clustering uncovering hidden content opportunities. This alignment of content creation with audience interests not only boosts website traffic but also improves search engine rankings.
Staying ahead of rapidly changing social media trends is complex and can affect marketing effectiveness. The solution is to analyze social media posts and comments and cluster them by similarity to detect trends, using algorithms such as DBSCAN to detect dense clusters representing hot topics. This approach enables businesses to stay ahead of trends and adapt marketing strategies, enhancing engagement with customers by aligning with current interests.
Managing and identifying duplicate content is time-consuming and leads to inconsistency. Clustering documents to identify identical or very similar content within a database, using techniques such as cosine similarity with K-Means, can detect duplicates. This method helps in managing content efficiently, avoiding redundancy, and ensuring consistency across platforms.
Understanding the structure and themes within a large dataset is challenging and crucial for informed decision-making. Using text clustering as a preliminary step to understand the structure and themes within a dataset, and combining embedding methods like GloVe with dimensionality reduction techniques like PCA to visualize clusters, provides insights into data. This supports decision-making by uncovering hidden patterns and relationships, guiding further analysis.
Text clustering serves as a powerful tool in the analytical arsenal, enabling organizations to make sense of vast amounts of unstructured text data. Through the intricate key steps of embedding documents, reducing dimensionality, and clustering embeddings, valuable insights can be extracted, driving decision-making and strategic planning. Whether it’s the simplicity of Bag-of-Words, the relevance weighing of TF-IDF, SBERT, or the semantic richness of Word Embeddings, each embedding technique offers unique avenues for analysis. Moreover, dimensionality reduction techniques like PCA, UMAP, and t-SNE play a vital role in transforming high-dimensional text into manageable, visualizable forms, each with its distinctive benefits and limitations.
The applications of text clustering are as diverse as the methods employed, ranging from identifying recurring support issues to optimizing knowledge management systems. Its adaptability and efficacy underscore the importance of text clustering in today’s data-driven world. As technology continues to evolve, so too will the techniques and algorithms, opening new horizons for exploration and innovation. Ultimately, text clustering stands as a testament to the incredible potential of data science and machine learning, turning the chaos of unstructured text into structured understanding, and unlocking opportunities across industries and domains.
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