Introduction
Topic modeling is a highly effective method in machine learning and natural language processing, which involves finding abstract subjects in a corpus of text. By analyzing the content of large collections of documents, topic modeling algorithms can uncover underlying themes and patterns that may not be immediately apparent. This article focuses on using BERT for subject modeling, instead of conventional techniques like Latent Dirichlet Allocation (LDA), latent semantic analysis, and non-negative matrix factorization.
Learning Objective
The learning objective for this topic modeling workshop using BERT includes:
1. Understanding the basics of topic modeling and its application in NLP.
2. Familiarizing oneself with BERT and how it creates document embeddings.
3. Preprocessing text data to prepare it for the BERT model.
4. Extracting document embeddings using the [CLS] token from the output of BERT.
5. Applying clustering methods like K-means to group related materials and find latent subjects.
6. Utilizing appropriate metrics to assess the quality of the generated topics.
By achieving these learning goals, participants will gain practical experience in using BERT for topic modeling, enabling them to analyze and extract hidden themes from large volumes of text data.
Load Data
The content used in this article is sourced from the Australian Broadcasting Corporation and is accessible on Kaggle. The dataset contains two significant columns: “publish_date” (the article’s publication date in yyyyMMdd format) and “headline_text” (the English translation of the headline’s text).
Topic Modeling with BERT
In this example, we will explore the key elements of BERT Topic and the necessary procedures to build a powerful topic model. We will use the BERTopic library and an embedding model called “paraphrase-MiniLM-L3-v2” to generate topic probabilities. The parameter “min_topic_size” is set to 7 to control the number of clusters or themes.
Topic Extraction and Representation
After fitting the BERTopic model with the headline text data, we can extract topic information using the “get_topic_info()” function. This provides insights into the number of topics and their respective word counts.
Topics Visualization
To gain a better understanding of each topic, we can visualize the topics using various techniques provided by BERTopic. These include creating bar charts of essential terms for each topic, generating intertopic distance maps, and visualizing topic hierarchies.
Search Topics
Once the topic model is trained, we can use the “find_topics” method to search for semantically related topics based on a given query word or phrase. This allows us to explore topics related to specific keywords and analyze their similarity scores.
Model Serialization & Loading
Finally, when satisfied with the model, it can be serialized and stored for future analysis. The BERTopic library provides functions for saving and loading serialized models.
Conclusion
Topic modeling using BERT offers a powerful method for identifying hidden topics in textual data. While BERT was initially developed for other NLP applications, it can be harnessed for topic modeling by leveraging document embeddings and clustering techniques. Understanding topic modeling with BERT allows data scientists, researchers, and analysts to extract and analyze underlying themes in large text corpora, leading to insightful conclusions and informed decision-making.
