Topic modeling is a part of natural language processing (NLP) which enables end-users to identify themes and topics within a collection of documents. It has applications in multiple industries for text mining and gaining relevant insights from textual data. This repository can be used as a reference to undertsand the math behind topic modeling, do a hands-on exercise of extracting topics from a sample data, use both pandas and pyspark libraries to perform data processing and topic modeling, how the end user can leverage these outcomes to take actionable insights and lastly, what are the necessary infrastructure required to implement a large-scale end-to-end solution.
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Section Topic modeling algorithms - explains the different topic modeling algorithms available.
Additional reading: Medium article on different topic modeling algorithms like LDA, NMF, BERTopic.
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Section Dataset used - provides information on the sample data that is used in this repository to perform topic modeling.
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Section Example results - shows the output of topic modeling on the above dataset. The analysis is done using both pandas LDA and SparkNLP LDA modules.
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Section End-to-end implementation showcases an example of a sample arcitecture of deploying such a large-scale solution to production. This also shows how multiple teams collaborate together for an enterprise-wide to build such solutions.
Additonal reading: Medium article on Implementation of end-to-end ML solution
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an unsupervised machine learning problem.
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does not aim to find similarities in documents, unlike text classification or clustering.
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makes clusters of three types of words – co-occurring words, distribution of words, and histogram of words topic-wise.
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Conventional and well-known approaches to topic modeling are:
§ Latent Semantic Analysis (LSA)
§ Probabilistic Latent Semantic Analysis (pLSA)
§ Latent Dirichlet Allocation (LDA)
§ Hierarchical Dirichlet Process (HDP)
§ Non-Matrix Factorization (NMF)
§ BERTopic
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basic principle behind the search of latent topics is the decomposition of the Document-Term Matrix (DTM) into a document-topic and a topic-term matrix. The three methods differ in how they define and reach this goal.
STEP 1:
Start with the conversion of a textual corpus into a Document -Term Matrix (DTM), a table where each row is a document, and each column is a distinct word.
Consider this corpus:
| document 1 | I like books |
| document 2 | I recently read two bestseller books |
| document 3 | Some movies are based on bestseller books |
The DOCUMENT-TERM MATRIX (DTM) for this corpus is
| I | like | books | recently | read | two | bestseller | some | movies | are | based | on | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| doc1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| doc2 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| doc3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 |
This matrix basically does a count of frequency of each of the words/terms in the documents. This is called the Term Frequency (TF).
As an alternative, Term Frequency – Inverse Document Frequency (TF-IDF) can be considered.
STEP 2:
Decompose the Document-Term Matrix DTM and extract topics.
- LSA uses matrix factorization - Singular Value Decomposition (SVD)
- pLSA uses probabilistic model, calculates the joint probability of seeing a word and a document together as a mixture of conditionally independent multinomial distributions
- LDA uses Dirichlet priors to estimate the document-topic and term-topic distributions in a Bayesian approach
The goal is to fins the Topic-Term Matrix by solving the equation
DTM (dim: Document-Topic Matrix (dim: Topic-Importance Matrix (dim: Topic-Term Matrix (dim:
Document-Term Matrix (DTM) (dim:
| term 1 | term 2 | ... | term n | |
|---|---|---|---|---|
| doc 1 | 1 | 0 | ||
| doc 2 | 0 | 1 | ||
| ... | ||||
| doc m |
Document-Topic Matrix (dim:
| topic 1 | topic 2 | topic 3 | |
|---|---|---|---|
| doc 1 | |||
| doc 2 |
Topic-Importance Matrix (dim:
| topic 1 | topic 2 | topic 3 | |
|---|---|---|---|
| topic 1 | |||
| topic 2 | |||
| topic 3 |
Topic-Term Matrix (dim:
| term 1 | term 2 | term 3 | |
|---|---|---|---|
| topic 1 | |||
| topic 2 | |||
| topic 3 |
where,
LDA is a generative probabilistic model for collections of discrete data such as text corpora.
- A 3-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics.
- Each topic is modeled as an infinite mixture over an underlying set of topic probabilities.
- The topic probabilities provide an explicit representation of a document.
Pros:
- better performances than LSA and pLSA - pLSA is prone to overfitting
- can assign a probability to a new document thanks to the document-topic Dirichlet distribution
- topics are open to human interpretation
Cons:
- number of topics must be known/set beforehand
- bag-of-words approach disregards the semantic representation of words in a corpus, similar to LSA and pLSA
- estimation of Bayes parameters lies under the assumption of exchangeability for the documents
- requires an extensive pre-processing phase to obtain a significant representation from the textual input data
- studies report LDA may yield too general (Rizvi et al., 2019) or irrelevant (Alnusyan et al., 2020) topics. Results may also be inconsistent across different executions (Egger et al., 2021).
The Kaggle dataset is downloaded from here. It has abstract and title for a set of research articles and each article is assigned to one or more of the following topics:
- Computer Science
- Physics
- Mathematics
- Statistics
- Quantitative Biology
- Quantitative Finance
Refer to this notebook for analysis in pandas.
SparkNLP module SparkNLP is developed and maintained by John Snow Labs. It is an open-source text processing module built on top of Apache Spark and spark ML library. It supports Python, Scala and Java.
PySpark ML module This contains dataframe-based ML Pipeline APIs which lets users quickly assemble and configure ML solutions. It is fast and uses distributed computing. To learn more about PySpark ML package, refer here.
Refer to this notebook for analysis in PySpark
Depending on the requirements of the end-users, the output format of the topics obtained may change. Usually, when topic modeling is done, the topic and the distribution of terms/words in the topic is obtained and presented as result.
| Topic | Topic terms | Topic weights/probablities |
|---|---|---|
| 1 | [term1, term2, term3] | [prob1, prob2, prob3 ] |
| 2 | [term1, term2, term3] | [prob1, prob2, prob3 ] |
| 3 | [term1, term2, term3] | [prob1, prob2, prob3 ] |
This can be then visualized in the notebook itself as shown below.
However it is also possible to map these output topics back to the original dataset, with each row showing which set of topic(s) it might belong to. This can be used if the requirement is that the end users want to drill down into each of the comments details.
| ID | Title | Abstract | Topic | Topic terms | Topic weights/probabilities |
|---|---|---|---|---|---|
| 1 | title 1 | abstract text | 1 | [term1, term2, term3] | [prob1, prob2, prob3 ] |
| 2 | title 2 | abstract text | 3 | [term1, term2, term3] | [prob1, prob2, prob3 ] |
| ... | |||||
| 500 | title 100 | abstract text | 5 | [term1, term2, term3] | [prob1, prob2, prob3 ] |
Both these output formats can be easily fed into one of the data visualization platforms like MS Power BI or Tableau to provide the end users with usable dashboards.
There are a lot of pieces involved while designing an end-to-end solution.
This is a sample data architecture of an end-to-end solution which is scheduled for refreshes.
The medallion structure is a best practice in industry nowadays.
- Deerwester, Scott, et al. “Indexing by latent semantic analysis.” Journal of the American society for information science 41.6 (1990): 391–407.
- Hofmann, Thomas. “Probabilistic latent semantic indexing.” In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. 1999.
- Blei, David M., Andrew Y. Ng, and Michael I. Jordan. “Latent dirichlet allocation.” Journal of machine Learning research 3, no. Jan (2003): 993–1022.
- Lee, Daniel, and H. Sebastian Seung. “Algorithms for non-negative matrix factorization.” Advances in neural information processing systems, 13 (2000).
- Lee, Daniel, and H. Sebastian Seung. “Learning the parts of objects by non-negative matrix factorization.” Nature 401, no. 6755 (1999): 788–791.
- Egger, Roman, and Joanne Yu. “A topic modeling comparison between lda, nmf, top2vec, and berttopic to demystify twitter posts.” Frontiers in sociology 7 (2022): 886498.
- Grootendorst, Maarten. “BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794 (2022).
- Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018).
- Shi, Tian, Kyeongpil Kang, Jaegul Choo, and Chandan K., Reddy. “Short-text topic modeling via non-negative matrix factorizationenriched with local word-context correlations.” In Proceedings of the 2018 World Wide Web Conference, pp. 1105–1114, 2018.
- Choo, Jaegul, Changhyun Lee, Chandan K. Reddy, and Haesun Park. “Utopian: User driven topic modeling based on interactive nonnegative matrix factorization.” IEEE transactions on visualization and computer graphics 19, np. 12 (2013): 1992: 2001.