are utilized in companies to categorise brand-related textual content datasets (corresponding to product and website critiques, surveys, and social media feedback) and to trace how buyer satisfaction metrics change over time.
There’s a myriad of current subject fashions one can select from: the broadly used BERTopic by Maarten Grootendorst (2022), the current FASTopic introduced ultimately 12 months’s NeurIPS, (Xiaobao Wu et al.,2024), the Dynamic Subject Mannequin by Blei and Lafferty (2006), or a contemporary semi-supervised Seeded Poisson Factorization mannequin (Prostmaier et al., 2025).
For a enterprise use case, coaching subject fashions on buyer texts, we frequently get outcomes that aren’t an identical and generally even conflicting. In enterprise, imperfections value cash, so the engineers ought to place into manufacturing the mannequin that gives the most effective answer and solves the issue most successfully. On the identical tempo that new subject fashions seem in the marketplace, strategies for evaluating their high quality utilizing new metrics additionally evolve.
This sensible tutorial will concentrate on bigram subject fashions, which offer extra related info and determine higher key qualities and issues for enterprise selections than single-word fashions (“supply” vs. “poor supply”, “abdomen” vs. “delicate abdomen”, and so on.). On one facet, bigram fashions are extra detailed; on the opposite, many analysis metrics weren’t initially designed for his or her analysis. To supply extra background on this space, we’ll discover intimately:
- Tips on how to consider the standard of bigram subject fashions
- Tips on how to put together an e mail classification pipeline in Python.
Our instance use case will present how bigram subject fashions (BERTopic and FASTopic) assist prioritize e mail communication with clients on sure matters and scale back response occasions.
1. What are subject mannequin high quality indicators?
The analysis activity ought to goal the best state:
The perfect subject mannequin ought to produce matters the place phrases or bigrams (two consecutive phrases) in every subject are extremely semantically associated and distinct for every subject.
In observe, which means the phrases predicted for every subject are semantically related to human judgment, and there may be low duplication of phrases between matters.
It’s commonplace to calculate a set of metrics for every educated mannequin to make a professional resolution on which mannequin to put into manufacturing or use for a enterprise resolution, evaluating the mannequin efficiency metrics.
- Coherence metrics consider how effectively the phrases found by a subject mannequin make sense to people (have related semantics in every subject).
- Subject range measures how totally different the found matters are from each other.
Bigram subject fashions work effectively with these metrics:
- NPMI (Normalized Level-wise Mutual Info) makes use of chances estimated in a reference corpus to calculate a [-1:1] rating for every phrase (or bigram) predicted by the mannequin. Learn [1] for extra particulars.
The reference corpus might be both inside (the coaching set) or exterior (e.g., an exterior e mail dataset). A big, exterior, and comparable corpus is a better option as a result of it may possibly assist scale back bias in coaching units. As a result of this metric works with phrase frequencies, the coaching set and the reference corpus must be preprocessed the identical method (i.e., if we take away numbers and stopwords within the coaching set, we must also do it within the reference corpus). The combination mannequin rating is the common of phrases throughout matters.
- SC (Semantic Coherence) doesn’t want a reference corpus. It makes use of the identical dataset as was used to coach the subject mannequin. Learn extra in [2].
Let’s say we’ve got the High 4 phrases for one subject: “apple”, “banana”, “juice”, “smoothie” predicted by a subject mannequin. Then SC appears in any respect mixtures of phrases within the coaching set going from left to proper, beginning with the primary phrase {apple, banana}, {apple, juice}, {apple, smoothie} then the second phrase {banana, juice}, {banana, smoothie}, then final phrase {juice, smoothie} and it counts the variety of paperwork that comprise each phrases, divided by the frequency of paperwork that comprise the primary phrase. Total SC rating for a mannequin is the imply of all topic-level scores.
PUV (Proportion of Distinctive Phrases) calculates the share of distinctive phrases throughout matters within the mannequin. PUV = 1 implies that every subject within the mannequin incorporates distinctive bigrams. Values near 1 point out a well-shaped, high-quality mannequin with small phrase overlap between matters. [3].
The nearer to 0 the SC and NIMP scores are, the extra coherent the mannequin is (bigrams predicted by the subject mannequin for every subject are semantically related). The nearer to 1 PUV is, the simpler the mannequin is to interpret and use, as a result of bigrams between matters don’t overlap.
2. How can we prioritize e mail communication with subject fashions?
A big share of buyer communication, not solely in e-commerce companies, is now solved with chatbots and private consumer sections. But, it’s common to speak with clients by e mail. Many e mail suppliers provide builders broad flexibility in APIs to customise their e mail platform (e.g., MailChimp, SendGrid, Brevo). On this place, subject fashions make mailing extra versatile and efficient.
On this use case, the pipeline takes the enter from the incoming emails and makes use of the educated subject classifier to categorize the incoming e mail content material. The result is the categorized subject that the Buyer Care (CC) Division sees subsequent to every e mail. The primary goal is to permit the CC workers to prioritize the classes of emails and scale back the response time to essentially the most delicate requests (that instantly have an effect on margin-related KPIs or OKRs).
3. Information and mannequin set-ups
We’ll practice FASTopic and Bertopic to categorise emails into 8 and 10 matters and consider the standard of all mannequin specs. Learn my earlier TDS tutorial on subject modeling with these cutting-edge subject fashions.
As a coaching set, we use a synthetically generated Buyer Care Electronic mail dataset out there on Kaggle with a GPL-3 license. The prefiltered information covers 692 incoming emails and appears like this:
3.1. Information preprocessing
Cleansing textual content in the fitting order is crucial for subject fashions to work in observe as a result of it minimizes the bias of every cleansing operation.
Numbers are sometimes eliminated first, adopted by emojis, except we don’t want them for particular conditions, corresponding to extracting sentiment. Stopwords for a number of languages are eliminated afterward, adopted by punctuation in order that stopwords don’t break up into two tokens (“we’ve” -> “we” + ‘ve”). Further tokens (firm and other people’s names, and so on.) are eliminated within the subsequent step within the clear information earlier than lemmatization, which unifies tokens with the identical semantics.
“Supply” and “deliveries”, “field” and “Bins”, or “Value” and “costs” share the identical phrase root, however with out lemmatization, subject fashions would mannequin them as separate elements. That’s why buyer emails must be lemmatized within the final step of preprocessing.
Textual content preprocessing is model-specific:
- FASTopic works with clear information on enter; some cleansing (stopwords) might be finished throughout the coaching. The best and best method is to make use of the Washer, a no-code app for textual content information cleansing that provides a no-code method of information preprocessing for textual content mining initiatives.
- BERTopic: the documentation recommends that “removing cease phrases as a preprocessing step shouldn’t be suggested because the transformer-based embedding fashions that we use want the total context to create correct embeddings”. For that reason, cleansing operations must be included within the mannequin coaching.
3.2. Mannequin compilation and coaching
You possibly can verify the total codes for FASTopic and BERTopic’s coaching with bigram preprocessing and cleansing in this repo. My earlier TDS tutorials (4) and (5) clarify all steps intimately.
We practice each fashions to categorise 8 matters in buyer e mail information. A easy inspection of the subject distribution exhibits that incoming emails to FASTopic are fairly effectively distributed throughout matters. BERTopic classifies emails inconsistently, maintaining outliers (uncategorized tokens) in T-1 and a big share of incoming emails in T0.
Listed here are the expected bigrams for each fashions with subject labels:
As a result of the e-mail corpus is an artificial LLM-generated dataset, the naive labelling of the matters for each fashions exhibits matters which are:
- Comparable: Time Delays, Latency Points, Person Permissions, Deployment Points, Compilation Errors,
- Differing: Unclassified (BERTopic classifies outliers into T-1), Enchancment Recommendations, Authorization Errors, Efficiency Complaints (FASTopic), Cloud Administration, Asynchronous Requests, Basic Requests (BERTopic)
For enterprise functions, matters must be labelled by the corporate’s insiders who know the shopper base and the enterprise priorities.
4. Mannequin analysis
If three out of eight categorized matters are labeled otherwise, then which mannequin must be deployed? Let’s now consider the coherence and variety for the educated BERTopic and FASTopic T-8 fashions.
4.1. NPMI
We’d like a reference corpus to calculate an NPMI for every mannequin. The Buyer IT Help Ticket Dataset from Kaggle, distributed with Attribution 4.0 Worldwide license, offers comparable information to our coaching set. The information is filtered to 11923 English e mail our bodies.
- Calculate an NPMI for every bigram within the reference corpus with this code.
- Merge bigrams predicted by FASTopic and BERTopic with their NPMI scores from the reference corpus. The less NaNs are within the desk, the extra correct the metric is.
3. Common NPMIs inside and throughout matters to get a single rating for every mannequin.
4.2. SC
With SC, we be taught the context and semantic similarity of bigrams predicted by a subject mannequin by calculating their place within the corpus in relation to different tokens. To take action, we:
- Create a document-term matrix (DTM) with a rely of what number of occasions every bigram seems in every doc.
- Calculate subject SC scores by trying to find bigram co-occurrences within the DTM and the bigrams predicted by subject fashions.
- Common subject SC to a mannequin SC rating.
4.3. PUV
Subject range PUV metric checks the duplicates of bigrams between matters in a mannequin.
- Be a part of bigrams into tokens by changing areas with underscores within the FASTopic and BERTopic tables of predicted bigrams.
2. Calculate subject range as rely of distinct tokens/ rely of tokens within the tables for each fashions.
4.4. Mannequin comparability
Let’s now summarize the coherence and variety analysis in Picture 9. BERTopic fashions are extra coherent however much less numerous than FASTopic. The variations are usually not very giant, however BERTopic suffers from uneven distribution of incoming emails into the pipeline (see charts in Picture 5). Round 32% of categorized emails fall into T0, and 15% into T-1, which covers the unclassified outliers. The fashions are educated with a min. of 20 tokens per subject. Growing this parameter causes the mannequin to be unable to coach, most likely due to the small information dimension.
For that reason, FASTopic is a better option for subject modelling in e mail classification with small coaching datasets.
The final step is to deploy the mannequin with subject labels within the e mail platform to categorise incoming emails:
Abstract
Coherence and variety metrics examine fashions with related coaching setups, the identical dataset, and cleansing technique. We can not examine their absolute values with the outcomes of various coaching classes. However they assist us determine on the most effective mannequin for our particular use case. They provide a relative comparability of varied mannequin specs and assist determine which mannequin must be deployed within the pipeline. Subject fashions analysis ought to all the time be the final step earlier than mannequin deployment in enterprise observe.
How does buyer care profit from the subject modelling train? After the subject mannequin is put into manufacturing, the pipeline sends a categorized subject for every e mail to the e-mail platform that Buyer Care makes use of for speaking with clients. With a restricted workers, it’s now potential to prioritize and reply quicker to essentially the most delicate enterprise requests (corresponding to “time delays” and “latency points”), and alter priorities dynamically.
Information and full codes for this tutorial are right here.
Petr Korab is a Python Engineer and Founding father of Textual content Mining Tales with over eight years of expertise in Enterprise Intelligence and NLP.
Acknowledgments: I thank Tomáš Horský (Lentiamo, Prague), Martin Feldkircher, and Viktoriya Teliha (Vienna Faculty of Worldwide Research) for helpful feedback and strategies.
References
[1] Blei, D. M., Lafferty, J. D. 2006. Dynamic subject fashions. In Proceedings of the twenty third worldwide convention on Machine studying (pp. 113–120).
[2] Dieng A.B., Ruiz F. J. R., and Blei D. M. 2020. Subject Modeling in embedding areas. Transactions of the Affiliation for Computational Linguistics, 8:439-453.
[3] Grootendorst, M. 2022. Bertopic: Neural Subject Modeling With A Class-Based mostly TF-IDF Process. Pc Science.
[4] Korab, P. Subject Modelling in Enterprise Intelligence: FASTopic and BERTopic in Code. In direction of Information Science. 22.1.2025. Accessible from: hyperlink.
[5] Korab, P. Subject Modelling with BERTtopic in Python. In direction of Information Science. 4.1.2024. Accessible from: hyperlink.
[6] Wu, X, Nguyen, T., Ce Zhang, D., Yang Wang, W., Luu, A. T. 2024. FASTopic: A Quick, Adaptive, Secure, and Transferable Subject Modeling Paradigm. arXiv preprint: 2405.17978.
[7] Mimno, D., Wallach, H., M., Talley, E., Leenders, M, McCallum. A. 2011. Optimizing Semantic Coherence in Subject Fashions. Proceedings of the 2011 Convention on Empirical Strategies in Pure Language Processing.
[8] Prostmaier, B., Vávra, J., Grün, B., Hofmarcher., P. 2025. Seeded Poisson Factorization: Leveraging area information to suit subject fashions. arXiv preprint: 2405.17978.