Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

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Topic Modeling with BERT

Topic Modeling with BERT

Key steps in BERTopic modelling are as following.

  • Use “Sentence Embedding” models to embed the sentences of the article
  • Reduce the dimensionality of embedding using UMAP
  • Cluster these documents (reduced dimensions) using HDBSAN
  • Use c-TF-IDF extract keywords, their frequency and IDF for each cluster.
  • MMR: Maximize Candidate Relevance. How many words in a topic can represent the topic?
  • Intertopic Distance Map
  • Use similarity matrix (heatmap), dandogram (hierarchical map), to visualize the topics and key_words.
  • Traction of topic over time period. Some may be irrelevant and for other traction may be increasing or decreasing.

Installation

# Installation, with sentence-transformers, can be done using pypi:

pip install bertopic

# If you want to install BERTopic with other embedding models, you can choose one of the following:

# Choose an embedding backend
pip install bertopic[flair, gensim, spacy, use]

# Topic modeling with images
pip install bertopic[vision]

Supported Topic Modelling Techniques

BERTopic supports all kinds of topic modeling techniques as below.

  • Guided
  • Supervised
  • Semi-supervised
  • Manual
  • Multi-topic distributions
  • Hierarchical
  • Class-based
  • Dynamic
  • Online/Incremental
  • Multimodal
  • Multi-aspect
  • Text Generation/LLM
  • Merge Models

Related Resources

Tools in BERTopic

Tools-in-BERTopic

Best Topic Modeling Tool in BERTopic

BEST-Tools-in-BERTopic

BERTopic Model Building

BERTopic-Model-Building

Application

  • arXiv Dataset (1.7m+ STEP papers)
  • Images/photographs
  • Historical Documents
  • News articles

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