{ "source_file": "0930", "source_platform": "Medium", "author": "Petr Koráb", "title_listed": "Topic Modelling with BERTtopic in Python", "keywords": "", "domain": "文本处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/topic-modelling-with-berttopic-in-python-8a80d529de34/", "publish_date": "2024-04-01", "corpus_stage": "", "notes": "", "id": "d034fb17723e", "collected_at": "2026-10-02T03:46:52", "title": "Topic Modelling with BERTtopic in Python | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Topic Modelling with BERTtopic in Python\nHands-on tutorial on modeling political statements with a state-of-the-art transformer-based topic model\n[Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\nApril 1, 20245 min read\n![Photo by Harryarts on Freepik](../images/000.jpeg)\nPhoto by [Harryarts](https://www.freepik.com/author/harryarts) on [Freepik](https://www.freepik.com/free-vector/modern-molecules-background_1186441.htm#fromView=search&page=2&position=26&uuid=5e3b0934-0d71-4782-bbf7-d1e739b45b27)\n _**Topic modeling**_ (i.e., topic identification in a corpus of text data) has developed quickly since the _Latent Dirichlet Allocation (LDA)_ model [was published](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf). This classic topic model, however, does not well capture the relationships between words because it is based on the statistical concept of a [bag of words](https://miningthedetails.com/LDA_Inference_Book/word-representations.html). Recent embedding-based [_Top2Vec_](https://github.com/ddangelov/Top2Vec) and _BERTopic_ models address its drawbacks by exploiting pre-trained language models to generate topics.\nIn this article, we'll use [Maarten Grootendorst's (2022)](https://arxiv.org/abs/2203.05794) **BERTopic** to identify the terms representing topics in political speech transcripts. It outperforms most traditional and modern topic models in topic modeling metrics on various corpora and has been used in [companies](https://maartengr.github.io/BERTopic/usecases.html#intelligent-virtual-assistants), academia ([Chagnon, 2024](https://www.sciencedirect.com/science/article/pii/S2949719123000419)), and the public sector. We'll explore in Python code:\n * how to effectively preprocess data\n * how to create a Bigram topic model\n * how to explore the most frequent terms over time.\n\n\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## 1. Example data\nAs an example dataset, we'll use the [_Empoliticon: Political Speeches-Context & Emotion dataset_](https://www.kaggle.com/datasets/efatazher/empoliticon-political-speeches-context-and-emotion), released under the _Attribution 4.0 International license,_ as part of the [Efat et al. (2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10141612) paper. It contains 2010 transcripts of political speeches from the presidents/ prime ministers of the USA, UK, China, and Russia. To make the topic model more focused, the subset only includes the 556 speeches of leaders from Russia:\n![Source: Emopoliticon: Political Speeches-Context & Emotion dataset](../images/body-f018744b29c3f408d18f.png)\nSource: Emopoliticon: Political Speeches-Context & Emotion dataset\n## 2. Data pre-processing\nWorking with text datasets is complex. Just cleaning involves several steps that should systematically remove all unnecessary information from the dataset. Check all [requirements ](https://github.com/PetrKorab/Topic-Modelling-with-BERTtopic-in-Python/blob/main/requirements.txt)for this project here.\n_**2.1. Fixing mojibake errors**_\n_Mojibake_ is a Japanese word for the confusing text that results from character-encoding errors. Here is an example:\n![Mojibake example](../images/001.png)\nMojibake example\nIt is useful to include this step right at the beginning of the cleaning. Correcting encoding-related errors is simple:\n_**2.2. Cleaning special characters, punctuation, and numbers**_\nThis step should come right after fixing the encoding errors. The simplest way is to use the [cleantext ](http://cleantext)library. Also, consider lower-casing. Does _\"labor\"_ mean the same as _\"Labor\"_ in the dataset? In case it does, add a `lowercase` parameter and apply the cleaning function:\n_**2.3. Define the stopwords removal strategy**_\nRemoving the standard list of stopwords is generally a necessary step. Depending on the project focus, it might also be useful to clean data from an additional list of stopwords that don't bring any value. As written in the [BERTopic's documentation:](https://maartengr.github.io/BERTopic/faq.html#how-do-i-reduce-topic-outliers)\n> _Removing stop words as a preprocessing step is not advised as the transformer-based embedding models that we use need the full context to create accurate embeddings._\nInstead, we use `CountVectorizer` to preprocess our documents **after** having generated embeddings **during** the topic **model generation.**\n## 3. Topic generation\nHaving a cleaner dataset, it is now possible to remove _English stopwords_ along with a list of _additional stopwords_ , generate a topic bigram model, and apply it to the data.\nNote that the `nr_topics`parameter is set to 7 for generating 6 topics. The remaining topic is used to keep the outliers.\n## 4. Topic visualization\nIn the next step, let's visualize the data in a heatmap to present the results better. Here is the outcome:\n![Figure 1: Heatmaps with bigrams and their probabilities, Image by Author](../images/002.png)\nFigure 1: Heatmaps with bigrams and their probabilities, Image by Author\nTo do so, we'll extract bigrams and their probabilities from the topic model and create a data frame for each of the 6 topics:\nNext, this code creates a heatmap in Figure 1.\n## 5. Token frequencies over time\nNow, we'll add a perspective on the development of bigrams over time. The goal is to look at which years the bigrams in the Russian leader(s) speeches were most frequently spoken out. The heatmap in Figure 2 displays the frequencies of the 5 most frequent bigrams for each year.\n![Figure 2: Heatmaps with bigrams and their frequencies by year, Image by Author](../images/003.png)\nFigure 2: Heatmaps with bigrams and their frequencies by year, Image by Author\nThe **arabica** library, which is now forthcoming in the [**Journal of Open Source Software**](https://joss.theoj.org/) (Koráb & Poměnková, 2024), was developed for this purpose.\n_**EDIT Jul 2024**_ _: Arabica has been updated. Check the_[ _**documentation**_](https://arabica.readthedocs.io/en/latest/index.html) _for the full list of parameters._\nHere is the code generating the heatmap in Figure 2:\n![Image by rawpixel on Freepik](../images/004.jpeg)\nImage by rawpixel on [Freepik](https://www.freepik.com/free-photo/improvement-summary-personal-development-workflow_18043786.htm#fromView=search&page=1&position=0&uuid=532be1da-a5ce-4d13-8dcb-141609e40365)\n## Conclusions\nThis article briefly introduced topic modeling with BERTopic. The model's framework offers many extensions, fine-tuning, and visualization methods (see [the documentation](https://maartengr.github.io/BERTopic/index.html)). Let's summarize the key findings:\n * topic models show 6 distinct topics for _**defense policy**_ (topic 1), _**economic development**_ (topic 2), _**WW2**_ (topic 3 ), _**internal policies**_ (topic 4), _**healthcare and demographics**_ (topic 5), and _**education**_ (topic 6).\n * combining BERTopic with Arabica, we can see that the **foreign** and **defense policy** topics (_\"armed forces\", \"russian federation\", \"law enforcement\"_) were more frequently discussed before 2012, while there is a shallow frequency of topics discussed related to **education** and **healthcare** , especially after 2010.\n * The dataset contains more **WW2, foreign,** and **defense policy** terms because Arabica returns absolute frequencies. However, it's difficult to interpret the results well without knowing the regional context.\n\n\n[My previous article ](https://medium.com/python-in-plain-english/end-to-end-latent-dirichlet-allocation-in-python-ac7bf75cd9fc)briefly explains a simpler approach to topic modeling with **LDA**. The complete code for this tutorial is on my [GitHub](https://github.com/PetrKorab/Topic-Modelling-with-BERTtopic-in-Python/blob/main/code.ipynb).\n_If you enjoy my work, you can invite me_[ _for coffee_](https://www.buymeacoffee.com/petrkorab) _and support my writing. You can also subscribe to my_[ _email list_](https://medium.com/subscribe/@petrkorab) _to get notified about my new articles. Thanks!_\n## References\n[1] Blei, Ng, Jordan (2003). Latent Dirichlet Allocation. [_Journal Of Machine Learning Research_](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) 3, pp. 993–1022.\n[2] Chagnon, Pandolfi, Donatelli, Ushizima (2024). Benchmarking topic models on scientific articles using BERTeley. [_Natural Language Processing Journal 6._](https://www.sciencedirect.com/science/article/pii/S2949719123000419)\n[3] Efat, Atiq, Abeed, Momin, Alam (2023). Empoliticon: NLP And MLBased Approach For Context And Emotion Classification Of Political Speeches From Transcripts. [_IEEE Access_](https://ieeexplore.ieee.org/document/10141612) _,_ vol. 11.\n[4] Grootendorst (2022). Bertopic: Neural Topic Modeling With A Class-Based TF-IDF Procedure. [_Computer Science._](https://arxiv.org/abs/2203.05794)\n[5] Koráb, Poměnková (2024). Arabica: A Python package for exploratory analysis of text data. In The Journal of Open Source Software. Journal of Open Source Software. .\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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This classic topic model, however, does not well capture the relationships between words because it is based on the statistical concept of a [bag of words](https://miningthedetails.com/LDA_Inference_Book/word-representations.html). Recent embedding-based [_Top2Vec_](https://github.com/ddangelov/Top2Vec) and _BERTopic_ models address its drawbacks by exploiting pre-trained language models to generate topics.\nIn this article, we'll use [Maarten Grootendorst's (2022)](https://arxiv.org/abs/2203.05794) **BERTopic** to identify the terms representing topics in political speech transcripts. It outperforms most traditional and modern topic models in topic modeling metrics on various corpora and has been used in [companies](https://maartengr.github.io/BERTopic/usecases.html#intelligent-virtual-assistants), academia ([Chagnon, 2024](https://www.sciencedirect.com/science/article/pii/S2949719123000419)), and the public sector. We'll explore in Python code:\n * how to effectively preprocess data\n * how to create a Bigram topic model\n * how to explore the most frequent terms over time.\n\n\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## 1. Example data\nAs an example dataset, we'll use the [_Empoliticon: Political Speeches-Context & Emotion dataset_](https://www.kaggle.com/datasets/efatazher/empoliticon-political-speeches-context-and-emotion), released under the _Attribution 4.0 International license,_ as part of the [Efat et al. (2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10141612) paper. It contains 2010 transcripts of political speeches from the presidents/ prime ministers of the USA, UK, China, and Russia. To make the topic model more focused, the subset only includes the 556 speeches of leaders from Russia:\n![Source: Emopoliticon: Political Speeches-Context & Emotion dataset](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1LCFi3YGrb44iEOzOpsAJVQ.png)\nSource: Emopoliticon: Political Speeches-Context & Emotion dataset\n## 2. Data pre-processing\nWorking with text datasets is complex. Just cleaning involves several steps that should systematically remove all unnecessary information from the dataset. Check all [requirements ](https://github.com/PetrKorab/Topic-Modelling-with-BERTtopic-in-Python/blob/main/requirements.txt)for this project here.\n_**2.1. Fixing mojibake errors**_\n_Mojibake_ is a Japanese word for the confusing text that results from character-encoding errors. Here is an example:\n![Mojibake example](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1E8ox-BHOf-_leq2FG9gCZQ.png)\nMojibake example\nIt is useful to include this step right at the beginning of the cleaning. Correcting encoding-related errors is simple:\n_**2.2. Cleaning special characters, punctuation, and numbers**_\nThis step should come right after fixing the encoding errors. The simplest way is to use the [cleantext ](http://cleantext)library. Also, consider lower-casing. Does _\"labor\"_ mean the same as _\"Labor\"_ in the dataset? In case it does, add a `lowercase` parameter and apply the cleaning function:\n_**2.3. Define the stopwords removal strategy**_\nRemoving the standard list of stopwords is generally a necessary step. Depending on the project focus, it might also be useful to clean data from an additional list of stopwords that don't bring any value. As written in the [BERTopic's documentation:](https://maartengr.github.io/BERTopic/faq.html#how-do-i-reduce-topic-outliers)\n> _Removing stop words as a preprocessing step is not advised as the transformer-based embedding models that we use need the full context to create accurate embeddings._\nInstead, we use `CountVectorizer` to preprocess our documents **after** having generated embeddings **during** the topic **model generation.**\n## 3. Topic generation\nHaving a cleaner dataset, it is now possible to remove _English stopwords_ along with a list of _additional stopwords_ , generate a topic bigram model, and apply it to the data.\nNote that the `nr_topics`parameter is set to 7 for generating 6 topics. The remaining topic is used to keep the outliers.\n## 4. Topic visualization\nIn the next step, let's visualize the data in a heatmap to present the results better. Here is the outcome:\n![Figure 1: Heatmaps with bigrams and their probabilities, Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/14J5McRW5ivv0NvlzT5JFvg.png)\nFigure 1: Heatmaps with bigrams and their probabilities, Image by Author\nTo do so, we'll extract bigrams and their probabilities from the topic model and create a data frame for each of the 6 topics:\nNext, this code creates a heatmap in Figure 1.\n## 5. Token frequencies over time\nNow, we'll add a perspective on the development of bigrams over time. The goal is to look at which years the bigrams in the Russian leader(s) speeches were most frequently spoken out. The heatmap in Figure 2 displays the frequencies of the 5 most frequent bigrams for each year.\n![Figure 2: Heatmaps with bigrams and their frequencies by year, Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1ZfCNTRW7m3LbB9zaxiHvrQ.png)\nFigure 2: Heatmaps with bigrams and their frequencies by year, Image by Author\nThe **arabica** library, which is now forthcoming in the [**Journal of Open Source Software**](https://joss.theoj.org/) (Koráb & Poměnková, 2024), was developed for this purpose.\n_**EDIT Jul 2024**_ _: Arabica has been updated. Check the_[ _**documentation**_](https://arabica.readthedocs.io/en/latest/index.html) _for the full list of parameters._\nHere is the code generating the heatmap in Figure 2:\n![Image by rawpixel on Freepik](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1XXD7PsD37A1OgZi5c0_ENA-scaled.jpeg)\nImage by rawpixel on [Freepik](https://www.freepik.com/free-photo/improvement-summary-personal-development-workflow_18043786.htm#fromView=search&page=1&position=0&uuid=532be1da-a5ce-4d13-8dcb-141609e40365)\n## Conclusions\nThis article briefly introduced topic modeling with BERTopic. The model's framework offers many extensions, fine-tuning, and visualization methods (see [the documentation](https://maartengr.github.io/BERTopic/index.html)). Let's summarize the key findings:\n * topic models show 6 distinct topics for _**defense policy**_ (topic 1), _**economic development**_ (topic 2), _**WW2**_ (topic 3 ), _**internal policies**_ (topic 4), _**healthcare and demographics**_ (topic 5), and _**education**_ (topic 6).\n * combining BERTopic with Arabica, we can see that the **foreign** and **defense policy** topics (_\"armed forces\", \"russian federation\", \"law enforcement\"_) were more frequently discussed before 2012, while there is a shallow frequency of topics discussed related to **education** and **healthcare** , especially after 2010.\n * The dataset contains more **WW2, foreign,** and **defense policy** terms because Arabica returns absolute frequencies. However, it's difficult to interpret the results well without knowing the regional context.\n\n\n[My previous article ](https://medium.com/python-in-plain-english/end-to-end-latent-dirichlet-allocation-in-python-ac7bf75cd9fc)briefly explains a simpler approach to topic modeling with **LDA**. The complete code for this tutorial is on my [GitHub](https://github.com/PetrKorab/Topic-Modelling-with-BERTtopic-in-Python/blob/main/code.ipynb).\n_If you enjoy my work, you can invite me_[ _for coffee_](https://www.buymeacoffee.com/petrkorab) _and support my writing. You can also subscribe to my_[ _email list_](https://medium.com/subscribe/@petrkorab) _to get notified about my new articles. Thanks!_\n## References\n[1] Blei, Ng, Jordan (2003). Latent Dirichlet Allocation. [_Journal Of Machine Learning Research_](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) 3, pp. 993–1022.\n[2] Chagnon, Pandolfi, Donatelli, Ushizima (2024). Benchmarking topic models on scientific articles using BERTeley. [_Natural Language Processing Journal 6._](https://www.sciencedirect.com/science/article/pii/S2949719123000419)\n[3] Efat, Atiq, Abeed, Momin, Alam (2023). Empoliticon: NLP And MLBased Approach For Context And Emotion Classification Of Political Speeches From Transcripts. [_IEEE Access_](https://ieeexplore.ieee.org/document/10141612) _,_ vol. 11.\n[4] Grootendorst (2022). Bertopic: Neural Topic Modeling With A Class-Based TF-IDF Procedure. [_Computer Science._](https://arxiv.org/abs/2203.05794)\n[5] Koráb, Poměnková (2024). Arabica: A Python package for exploratory analysis of text data. In The Journal of Open Source Software. Journal of Open Source Software. .\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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