# BERTrend
[](https://github.com/rte-france/BERTrend "Go to GitHub repo")
[](https://github.com/rte-france/BERTrend)
[](https://github.com/rte-france/BERTrend)
[](https://github.com/rte-france/BERTrend/issues)
[](https://pepy.tech/project/bertrend)
[](https://pypi.org/project/bertrend/)
## Paper
The code in this repository is part of the work described in the paper:
[BERTrend: Neural Topic Modeling for Emerging Trends Detection](./BERTrend%20paper.pdf).
## Description
BERTrend is a novel framework for detecting and monitoring weak signals in large, evolving text corpora. It uses neural topic modeling, specifically [BERTopic](https://github.com/MaartenGr/BERTopic), in an online learning setting to identify and track topic evolution over time.
BERTrend classifies topics as noise, weak signals, or strong signals based on their popularity trends, using a metric that considers both the number of documents and update frequency. This method allows for real-time monitoring and analysis of emerging trends while filtering out noise.
## Main Features
The BERTrend repository provides a set of metrics, methods, and visualization tools to support the analysis of dynamic topic modeling in large text corpora. The tools are designed to help users detect, analyze, and visualize emerging topics and trends over time.
The code base is illustrated via two main demos:
### Dynamic Topic Modeling Analysis
- [bertrend/demos/topic_analysis](bertrend/demos/topic_analysis): Focuses on dynamic topic modeling analysis using:
- Dedicated metrics, called TEMPTopic:
- Stability Evaluation: Measures the stability of topics over time, evaluating how consistent and coherent topics remain.
- Volatility Assessment: Analyzes the volatility of topics, identifying how much topics change over different time periods.
- Two Perspectives:
- Temporal Topic Embedding Stability: Evaluates the stability of topic embeddings over time.
- Temporal Topic Representation Stability: Assesses the stability of topic representations over time.
- Visualization tools to easily assess the results.
#### Topic Modeling Analysis Demonstrator Video
https://github.com/user-attachments/assets/f600f666-a6da-40be-8b07-5041b3bde1dc
#### Demonstrator Features
- Data management: selection of single/multiple datasets, filter by time, filter by size, split long documents by paragraphs
- Customization of BERTopic parameters
- Exploration of topics by source, generate topic summaries using specific sources
- Generate and customize newsletters by applying LLMs on BERTopic's output topics
- Visualize and explore topics via different types of interactive figures (topics treemap, data maps)
- Explore the evolution of topics in a temporal semantic space
- Explore the stability of topics measured by TEMPTopic
### Weak Signal Analysis
- [bertrend/demos/weak_signals](bertrend/demos/weak_signals): Identifies and analyzes emerging trends and signals
#### Weak Signal Analysis Demonstrator Video
https://github.com/user-attachments/assets/d79368d9-d4e0-4324-8a98-a888f0ab3b65
#### Demonstrator Features
- Exploration of signals in real time at any given timestamp
- Categorization of signals into three different dataframes: noise, weak and strong signals
(each dataframe contains useful information about the signals)
- Interpretation of these signals using LLMs
- Inspection of the topic merging process using Sankey diagram
## Installation
### Environment Variables
BERTrend requires the following environment variables to be set:
- `BERTREND_BASE_DIR`: the home directory for BERTrend data, models, and logs
- OpenAI variables (for LLM integration):
- `OPENAI_API_KEY`: the API key for your LLM deployment
- `OPENAI_ENDPOINT`: the endpoint for your LLM deployment (empty if using OpenAI services)
- `OPENAI_DEFAULT_MODEL_NAME`: the name of the default LLM model to use
### Hardware and Software Requirements
- Recommended: at least one GPU with 16GB RAM for faster processing
- Python >= 3.12 is required
### Package Installation
- (Recommended): create a new python environment using `venv` or `conda`:
`python -m venv <path>/bertrend`
- Installation from pypi
`pip install bertrend`
- Install using pip or poetry in the BERTrend home directory:
```bash
# Basic installation with core dependencies
pip install . # or poetry install
# Installation with apps dependencies
pip install ".[apps]" # or poetry install --extras apps
```
- (Use `pip install -e .` for a local installation)
NB. Due to the number of dependencies, the installation may take some time on an empty environment.
### Dependency Management
BERTrend's dependencies are organized into logical groups for easier management:
- **Core dependencies**: Essential packages for topic modeling and analysis
- **NLP and text processing**: Libraries for natural language processing tasks
- **LLM integration**: Components for working with large language models
- **Visualization and UI**: Tools for data visualization and user interfaces
- **Utilities**: General utility libraries
Optional dependency groups:
- **test**: Dependencies for running tests and measuring code coverage
- **apps**: Additional dependencies required for BERTrend applications
This organization simplifies installation and maintenance, allowing users to install only the dependencies they need for their specific use case.
### Verifying Installation
To verify that your installation is working correctly, you can run the installation verification script:
```bash
python -m bertrend.tests.test_installation
```
This script will check that all dependencies are installed correctly and provide a summary of the installation status. If any dependencies are missing, the script will provide instructions on how to install them.
### Datasets
#### Dataset Sources
BERTrend can work with various text datasets such as:
- Scientific publications
- News articles
- Social media publications
Recommended public datasets:
- New York Times dataset: https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present
- Arxiv dataset: https://www.kaggle.com/datasets/Cornell-University/arxiv
For custom dataset creation, please refer to the documentation page about [data providers](./docs/data_provider.md).
In order to use the provided demos, datasets :
- can be stored on the server in: `$BERTREND_BASE_DIR/data/bertrend/`
- or can be uploaded from the client device on which are displayed the Streamlit apps.
#### Dataset Format
Required columns:
- `text`
- `timestamp`
Supported formats:
- csv
- xlsx
- parquet
- json/jsonl
## Quick Start
### 1. Dynamic Topic Modeling Analysis
#### Launch the Demonstrator
```bash
cd bertrend/demos/topic_analysis
CUDA_VISIBLE_DEVICES=<gpu_number> streamlit run app.py
```
#### Description of Main Pages
- `Parameters`: Include all BERTopic parameters. Choose the embedding model carefully based on your data language.
The embedding model can run locally (a GPU is recommended); it is also possible to use a remote embedding service.
We recommend using a remote embedding service (this is now the default config) as it simplifies the computing.
The change of configuration can be done in: [services_default_config.toml](bertrend/config/services_default_config.toml).
An example of a very simple implementation to deploy a separate embedding service can be found here: `bertrend/services/embedding_server`
Please refer to the [BERTopic documentation](https://maartengr.github.io/BERTopic/algorithm/algorithm.html) for parameter details.
The application is split into several pages:
- `Data loading & model training`: Allows to
- Select a dataset
- Select the range of timestamps
- Split paragraphs if needed
- Train a topic model
- `Topic exploration`
- Computes topics over time
- Allow selecting sources and generating short descriptions for each theme
- `Topic visualization`: Several visualization types available:
- `Overall Results`: Standard information such as inter-topic distance map
- `Topics Treemap`: Block view of topics and their relative importance
- `Data Map`: Easy topic and data search interface
- `Temporal Visualization`: Focus on dynamic topic modeling:
- 3D visualization of temporal topic evolution
- Metrics to assess topic stability over time
- Overall topic stability, temporal topic stability, and temporal representation stability
- `Generate Newsletters`
- This is an example of an application combining topic analysis and LLMs
- Generates newsletters based on prominent topics
- Uses LLMs to generate topic descriptions and summarize representative documents
### 2. Weak Signal Analysis
**Weak signals** refer to early, small indicators of larger, important trends (like disruptive technologies or public health issues).
Identifying weak signals early is critical for decision-making across domains such as politics, healthcare, and innovation
Key Features of the demonstrator:
- Dynamic Topic Modeling in an online learning setting
- Popularity-based Signal Classification
* based on their popularity trends: noise, weak signals, and strong signals
* **new popularity metric** considers both the number of documents within a topic and the **frequency of updates**
#### Launch the Weak Signal Analysis Demonstrator
```bash
cd bertrend/demos/weak_signals
CUDA_VISIBLE_DEVICES=<gpu_number> streamlit run app.py
```
#### Data Preparation
- Choose from available datasets
- Click on `Embed Documents`
#### Train Model
- Select the `Model Training` tab
- Choose time granularity (in days)
- Click `Train Models` to generate topic models per time slice
- Click `Merge Models` for the aggregated model
#### Results Analysis
Provides various statistics about topics:
- Number of topics detected
- Topic size evolution
- Topic popularity evolution
- Overall popularity evolution
- Lists of noise, weak, and strong signals
#### LLM-based signal interpretation
- As topics are hard to interprete by humans, we showcase how it can be
done using LLMs.
- Choose a specific topic and click on the button to generate an interpretation
- LLM-based in-depth signal analysis is done using predefined templates, for example:
- describe what's happened since the last timestamp (summary of main events)
- present through a multi-faceted description info about key developments, foreseen changes, potential impact, etc.
## Support
Please use the 'Issues' section to report and discuss any problems with BERTrend.
## Contributing
We welcome contributions. For major changes, please open an issue first to discuss your proposal.
To contribute:
1. Fork the repository at https://github.com/rte-france/BERTrend
2. Sync your fork with the latest development branch
3. Implement your changes
4. Add tests and documentation if applicable
5. Sync with the latest development branch again
6. Submit a pull request targeting the appropriate branch
## Authors and Acknowledgment
See [AUTHORS](./AUTHORS.txt) for the full list of contributors.
## License Information
Copyright 2023–2025 RTE France RTE: http://www.rte-france.com
This Source Code is subject to the terms of the Mozilla Public License (MPL) v2 also available here: https://www.mozilla.org/en-US/MPL/2.0/
## Citation
If you use BERTrend in your work, please cite:
```bibtex
@inproceedings{boutaleb-etal-2024-bertrend,
title = "{BERT}rend: Neural Topic Modeling for Emerging Trends Detection",
author = "Boutaleb, Allaa and
Picault, Jerome and
Grosjean, Guillaume",
editor = "Tetreault, Joel and
Nguyen, Thien Huu and
Lamba, Hemank and
Hughes, Amanda",
booktitle = "Proceedings of the Workshop on the Future of Event Detection (FuturED)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.futured-1.1",
pages = "1--17",
}
```
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"description": "# BERTrend\n\n[](https://github.com/rte-france/BERTrend \"Go to GitHub repo\")\n[](https://github.com/rte-france/BERTrend)\n[](https://github.com/rte-france/BERTrend)\n[](https://github.com/rte-france/BERTrend/issues)\n[](https://pepy.tech/project/bertrend)\n[](https://pypi.org/project/bertrend/)\n\n\n\n\n\n## Paper\n\nThe code in this repository is part of the work described in the paper: \n[BERTrend: Neural Topic Modeling for Emerging Trends Detection](./BERTrend%20paper.pdf).\n\n## Description\n\nBERTrend is a novel framework for detecting and monitoring weak signals in large, evolving text corpora. It uses neural topic modeling, specifically [BERTopic](https://github.com/MaartenGr/BERTopic), in an online learning setting to identify and track topic evolution over time. \n\nBERTrend classifies topics as noise, weak signals, or strong signals based on their popularity trends, using a metric that considers both the number of documents and update frequency. This method allows for real-time monitoring and analysis of emerging trends while filtering out noise.\n\n\n## Main Features\n\nThe BERTrend repository provides a set of metrics, methods, and visualization tools to support the analysis of dynamic topic modeling in large text corpora. The tools are designed to help users detect, analyze, and visualize emerging topics and trends over time.\n\nThe code base is illustrated via two main demos:\n\n### Dynamic Topic Modeling Analysis\n\n- [bertrend/demos/topic_analysis](bertrend/demos/topic_analysis): Focuses on dynamic topic modeling analysis using:\n - Dedicated metrics, called TEMPTopic:\n - Stability Evaluation: Measures the stability of topics over time, evaluating how consistent and coherent topics remain.\n - Volatility Assessment: Analyzes the volatility of topics, identifying how much topics change over different time periods.\n - Two Perspectives:\n - Temporal Topic Embedding Stability: Evaluates the stability of topic embeddings over time.\n - Temporal Topic Representation Stability: Assesses the stability of topic representations over time.\n - Visualization tools to easily assess the results.\n\n#### Topic Modeling Analysis Demonstrator Video\nhttps://github.com/user-attachments/assets/f600f666-a6da-40be-8b07-5041b3bde1dc\n\n#### Demonstrator Features\n- Data management: selection of single/multiple datasets, filter by time, filter by size, split long documents by paragraphs \n- Customization of BERTopic parameters\n- Exploration of topics by source, generate topic summaries using specific sources\n- Generate and customize newsletters by applying LLMs on BERTopic's output topics\n- Visualize and explore topics via different types of interactive figures (topics treemap, data maps)\n- Explore the evolution of topics in a temporal semantic space\n- Explore the stability of topics measured by TEMPTopic\n\n### Weak Signal Analysis\n\n- [bertrend/demos/weak_signals](bertrend/demos/weak_signals): Identifies and analyzes emerging trends and signals\n\n#### Weak Signal Analysis Demonstrator Video\nhttps://github.com/user-attachments/assets/d79368d9-d4e0-4324-8a98-a888f0ab3b65\n\n\n#### Demonstrator Features\n- Exploration of signals in real time at any given timestamp\n- Categorization of signals into three different dataframes: noise, weak and strong signals\n (each dataframe contains useful information about the signals)\n- Interpretation of these signals using LLMs\n- Inspection of the topic merging process using Sankey diagram\n\n## Installation\n\n### Environment Variables\n\nBERTrend requires the following environment variables to be set:\n\n- `BERTREND_BASE_DIR`: the home directory for BERTrend data, models, and logs\n- OpenAI variables (for LLM integration):\n - `OPENAI_API_KEY`: the API key for your LLM deployment\n - `OPENAI_ENDPOINT`: the endpoint for your LLM deployment (empty if using OpenAI services)\n - `OPENAI_DEFAULT_MODEL_NAME`: the name of the default LLM model to use\n\n### Hardware and Software Requirements\n\n- Recommended: at least one GPU with 16GB RAM for faster processing\n- Python >= 3.12 is required\n\n### Package Installation\n\n- (Recommended): create a new python environment using `venv` or `conda`: \n\n `python -m venv <path>/bertrend`\n\n\n- Installation from pypi\n\n `pip install bertrend` \n\n\n- Install using pip or poetry in the BERTrend home directory:\n\n ```bash\n # Basic installation with core dependencies\n pip install . # or poetry install\n\n # Installation with apps dependencies\n pip install \".[apps]\" # or poetry install --extras apps\n\n ```\n\n- (Use `pip install -e .` for a local installation)\n\nNB. Due to the number of dependencies, the installation may take some time on an empty environment.\n\n### Dependency Management\n\nBERTrend's dependencies are organized into logical groups for easier management:\n\n- **Core dependencies**: Essential packages for topic modeling and analysis\n- **NLP and text processing**: Libraries for natural language processing tasks\n- **LLM integration**: Components for working with large language models\n- **Visualization and UI**: Tools for data visualization and user interfaces\n- **Utilities**: General utility libraries\n\nOptional dependency groups:\n\n- **test**: Dependencies for running tests and measuring code coverage\n- **apps**: Additional dependencies required for BERTrend applications\n\nThis organization simplifies installation and maintenance, allowing users to install only the dependencies they need for their specific use case.\n\n### Verifying Installation\n\nTo verify that your installation is working correctly, you can run the installation verification script:\n\n```bash\npython -m bertrend.tests.test_installation\n```\n\nThis script will check that all dependencies are installed correctly and provide a summary of the installation status. If any dependencies are missing, the script will provide instructions on how to install them.\n\n### Datasets\n\n#### Dataset Sources\nBERTrend can work with various text datasets such as:\n- Scientific publications\n- News articles\n- Social media publications\n\nRecommended public datasets:\n- New York Times dataset: https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present\n- Arxiv dataset: https://www.kaggle.com/datasets/Cornell-University/arxiv\n\nFor custom dataset creation, please refer to the documentation page about [data providers](./docs/data_provider.md).\n\n\nIn order to use the provided demos, datasets :\n- can be stored on the server in: `$BERTREND_BASE_DIR/data/bertrend/`\n- or can be uploaded from the client device on which are displayed the Streamlit apps.\n\n#### Dataset Format\nRequired columns:\n- `text`\n- `timestamp`\n\nSupported formats:\n- csv\n- xlsx\n- parquet\n- json/jsonl\n\n## Quick Start\n\n### 1. Dynamic Topic Modeling Analysis\n\n#### Launch the Demonstrator\n```bash\ncd bertrend/demos/topic_analysis\nCUDA_VISIBLE_DEVICES=<gpu_number> streamlit run app.py\n```\n\n#### Description of Main Pages\n- `Parameters`: Include all BERTopic parameters. Choose the embedding model carefully based on your data language.\nThe embedding model can run locally (a GPU is recommended); it is also possible to use a remote embedding service.\n\nWe recommend using a remote embedding service (this is now the default config) as it simplifies the computing.\n\nThe change of configuration can be done in: [services_default_config.toml](bertrend/config/services_default_config.toml).\n\nAn example of a very simple implementation to deploy a separate embedding service can be found here: `bertrend/services/embedding_server`\n\n\n\n\nPlease refer to the [BERTopic documentation](https://maartengr.github.io/BERTopic/algorithm/algorithm.html) for parameter details.\n\nThe application is split into several pages:\n\n- `Data loading & model training`: Allows to \n - Select a dataset\n - Select the range of timestamps\n - Split paragraphs if needed\n - Train a topic model\n\n\n\n- `Topic exploration`\n - Computes topics over time\n \n\n - Allow selecting sources and generating short descriptions for each theme\n \n\n- `Topic visualization`: Several visualization types available:\n - `Overall Results`: Standard information such as inter-topic distance map\n - `Topics Treemap`: Block view of topics and their relative importance\n - `Data Map`: Easy topic and data search interface\n\n  \n\n- `Temporal Visualization`: Focus on dynamic topic modeling:\n - 3D visualization of temporal topic evolution\n - Metrics to assess topic stability over time\n - Overall topic stability, temporal topic stability, and temporal representation stability\n\n  \n\n- `Generate Newsletters`\n - This is an example of an application combining topic analysis and LLMs\n - Generates newsletters based on prominent topics\n - Uses LLMs to generate topic descriptions and summarize representative documents\n\n\n### 2. Weak Signal Analysis\n**Weak signals** refer to early, small indicators of larger, important trends (like disruptive technologies or public health issues).\n\nIdentifying weak signals early is critical for decision-making across domains such as politics, healthcare, and innovation\n\nKey Features of the demonstrator:\n- Dynamic Topic Modeling in an online learning setting\n- Popularity-based Signal Classification\n * based on their popularity trends: noise, weak signals, and strong signals\n * **new popularity metric** considers both the number of documents within a topic and the **frequency of updates**\n\n\n#### Launch the Weak Signal Analysis Demonstrator\n```bash\ncd bertrend/demos/weak_signals\nCUDA_VISIBLE_DEVICES=<gpu_number> streamlit run app.py\n```\n\n#### Data Preparation\n- Choose from available datasets\n\n- Click on `Embed Documents`\n\n#### Train Model\n- Select the `Model Training` tab\n- Choose time granularity (in days)\n- Click `Train Models` to generate topic models per time slice\n- Click `Merge Models` for the aggregated model\n\n#### Results Analysis\nProvides various statistics about topics:\n- Number of topics detected\n- Topic size evolution\n- Topic popularity evolution\n- Overall popularity evolution\n\n\n\n- Lists of noise, weak, and strong signals\n\n#### LLM-based signal interpretation\n- As topics are hard to interprete by humans, we showcase how it can be\ndone using LLMs.\n- Choose a specific topic and click on the button to generate an interpretation\n- LLM-based in-depth signal analysis is done using predefined templates, for example:\n - describe what's happened since the last timestamp (summary of main events)\n - present through a multi-faceted description info about key developments, foreseen changes, potential impact, etc.\n\n\n\n\n## Support\n\nPlease use the 'Issues' section to report and discuss any problems with BERTrend.\n\n## Contributing\n\nWe welcome contributions. For major changes, please open an issue first to discuss your proposal.\n\nTo contribute:\n1. Fork the repository at https://github.com/rte-france/BERTrend\n2. Sync your fork with the latest development branch\n3. Implement your changes\n4. Add tests and documentation if applicable\n5. Sync with the latest development branch again\n6. Submit a pull request targeting the appropriate branch\n\n## Authors and Acknowledgment\n\nSee [AUTHORS](./AUTHORS.txt) for the full list of contributors.\n\n## License Information\n\nCopyright 2023\u20132025 RTE France RTE: http://www.rte-france.com\n\nThis Source Code is subject to the terms of the Mozilla Public License (MPL) v2 also available here: https://www.mozilla.org/en-US/MPL/2.0/\n\n## Citation\n\nIf you use BERTrend in your work, please cite:\n\n```bibtex\n@inproceedings{boutaleb-etal-2024-bertrend,\n title = \"{BERT}rend: Neural Topic Modeling for Emerging Trends Detection\",\n author = \"Boutaleb, Allaa and\n Picault, Jerome and\n Grosjean, Guillaume\",\n editor = \"Tetreault, Joel and\n Nguyen, Thien Huu and\n Lamba, Hemank and\n Hughes, Amanda\",\n booktitle = \"Proceedings of the Workshop on the Future of Event Detection (FuturED)\",\n month = nov,\n year = \"2024\",\n address = \"Miami, Florida, USA\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2024.futured-1.1\",\n pages = \"1--17\",\n}\n\n```\n\n",
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