stream-topic 0.3.0
A python package for expanded topic modeling and metrics
License: MIT
Requires Python: >=3.6

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        [📘Documentation](https://stream-topic.readthedocs.io/en/latest/index.html) |
        [🛠️Installation](https://stream-topic.readthedocs.io/en/latest/installation.html) |
        [Models](https://stream-topic.readthedocs.io/en/latest/api/models/index.html) |
        [🤔Report Issues](https://github.com/AnFreTh/STREAM/issues)
        </div>
        
        
        <h1 style="text-align: center;">STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module</h1>
        
        <h3 style="text-align: center;">- Topic Modeling Made Easy in Python -</h3>
        
        
        <p>We present STREAM, a Simplified Topic Retrieval, Exploration, and Analysis Module for User-Friendly and Interactive Topic Modeling and Visualization. Our paper can be found <a href="https://aclanthology.org/2024.acl-short.41.pdf">here</a>.</p>
        
        
        <h2> Table of Contents </h2>
        
        
        - [🏃 Quick Start](#-quick-start)
        - [🚀 Installation](#-installation)
        - [📦 Available Models](#-available-models)
        - [📊 Available Metrics](#-available-metrics)
        - [🗂️ Available Datasets](#️-available-datasets)
        - [🔧 Usage](#-usage)
          - [🛠️ Preprocessing](#️-preprocessing)
          - [🚀 Model fitting](#-model-fitting)
          - [✅ Evaluation](#-evaluation)
          - [🔍 Hyperparameter optimization](#-hyperparameter-optimization)
          - [🖼️ Visualization](#️-visualization)
          - [📈 Downstream Tasks](#-downstream-tasks)
          - [🧪 Experimental 🧪](#-experimental-)
          - [🤝 Contributing and Testing New Models](#-contributing-and-testing-new-models)
        - [📜 Citation](#-citation)
        - [📝 License](#-license)
        
        
        # 🏃 Quick Start
        
        Get started with STREAM in just a few lines of code:
        
        ```python
        from stream_topic.models import KmeansTM
        from stream_topic.utils import TMDataset
        
        dataset = TMDataset()
        dataset.fetch_dataset("BBC_News")
        dataset.preprocess(model_type="KmeansTM")
        
        model = KmeansTM()
        model.fit(dataset, n_topics=20)
        
        topics = model.get_topics()
        print(topics)
        ```
        
        
        # 🚀 Installation
        
        You can install STREAM directly from PyPI or from the GitHub repository:
        
        1. **PyPI (Recommended)**:
            ```bash
            pip install stream-topic
            ```
        
        2. **GitHub**:
            ```bash
            pip install git+https://github.com/AnFreTh/STREAM.git
            ```
        
        3. **Download necessary NLTK resources**:
        
           To download all necessary NLTK resources required for some models, simply run:
        
           ```python
           import nltk
        
           def ensure_nltk_resources():
               resources = [
                   "stopwords",
                   "wordnet",
                   "punkt_tab",
                   "brown",
                   "averaged_perceptron_tagger"
               ]
               for resource in resources:
                   try:
                       nltk.data.find(resource)
                   except LookupError:
                       try:
                           print(f"Downloading NLTK resource: {resource}")
                           nltk.download(resource)
                       except Exception as e:
                           print(f"Failed to download {resource}: {e}")
        
           ensure_nltk_resources()
           ```
        
        
        3. **Install requirements for add-ons**:
            To use STREAMS visualizations, simply run:
            ```bash
            pip install stream-topic[plotting]
            ```
        
            For BERTopic, run:
            ```bash
            pip install stream-topic[hdbscan]
            ```
        
            For DCTE:
            ```bash
            pip install stream-topic[dcte]
            ```
        
            For the experimental features:
            ```bash
            pip install stream-topic[experimental]
            ```
        
        # 📦 Available Models
        STREAM offers a variety of neural as well as non-neural topic models and we are always trying to incorporate more and new models. If you wish to incorporate your own model, or want another model incorporated please raise an issue with the required information. Currently, the following models are implemented:
        
        <div align="center" style="width: 100%;">
          <table style="margin: 0 auto;">
            <thead>
              <tr>
                <th><strong>Name</strong></th>
                <th><strong>Implementation</strong></th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td><a href="https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf?ref=http://githubhelp.com">LDA</a></td>
                <td>Latent Dirichlet Allocation</td>
              </tr>
              <tr>
                <td><a href="https://www.nature.com/articles/44565">NMF</a></td>
                <td>Non-negative Matrix Factorization</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/abs/2004.14914">WordCluTM</a></td>
                <td>Tired of topic models?</td>
              </tr>
              <tr>
                <td><a href="https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00506/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality?searchresult=1">CEDC</a></td>
                <td>Topics in the Haystack</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/pdf/2212.09422.pdf">DCTE</a></td>
                <td>Human in the Loop</td>
              </tr>
              <tr>
                <td><a href="https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00506/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality?searchresult=1">KMeansTM</a></td>
                <td>Simple Kmeans followed by c-tfidf</td>
              </tr>
              <tr>
                <td><a href="https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b3c81b523b1f03c87192aa2abbf9ffb81a143e54">SomTM</a></td>
                <td>Self organizing map followed by c-tfidf</td>
              </tr>
              <tr>
                <td><a href="https://ieeexplore.ieee.org/abstract/document/10066754">CBC</a></td>
                <td>Coherence based document clustering</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/pdf/2403.03737">TNTM</a></td>
                <td>Transformer-Representation Neural Topic Model</td>
              </tr>
              <tr>
                <td><a href="https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00325/96463/Topic-Modeling-in-Embedding-Spaces">ETM</a></td>
                <td>Topic modeling in embedding spaces</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/abs/2004.03974">CTM</a></td>
                <td>Combined Topic Model</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/abs/2303.14951">CTMNeg</a></td>
                <td>Contextualized Topic Models with Negative Sampling</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/abs/1703.01488">ProdLDA</a></td>
                <td>Autoencoding Variational Inference For Topic Models</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/abs/1703.01488">NeuralLDA</a></td>
                <td>Autoencoding Variational Inference For Topic Models</td>
              </tr>
              <tr>
                <td><a href="https://arxiv.org/abs/2008.13537">NSTM</a></td>
                <td>Neural Topic Model via Optimal Transport</td>
              </tr>
            </tbody>
          </table>
        </div>
        
        
        
        # 📊 Available Metrics
        Since evaluating topic models, especially automatically, STREAM implements numerous evaluation metrics. Especially, the intruder based metrics, while they might take some time to compute, have shown great correlation with human evaluation. 
        <div align="center" style="width: 100%;">
          <table style="margin: 0 auto;">
          <thead>
            <tr>
              <th><strong>Name</strong></th>
              <th><strong>Description</strong></th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td><a href="https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00506/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality?searchresult=1">ISIM</a></td>
              <td>Average cosine similarity of top words of a topic to an intruder word.</td>
            </tr>
            <tr>
              <td><a href="https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00506/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality?searchresult=1">INT</a></td>
              <td>For a given topic and a given intruder word, Intruder Accuracy is the fraction of top words to which the intruder has the least similar embedding among all top words.</td>
            </tr>
            <tr>
              <td><a href="https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00506/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality?searchresult=1">ISH</a></td>
              <td>Calculates the shift in the centroid of a topic when an intruder word is replaced.</td>
            </tr>
            <tr>
              <td><a href="https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00506/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality?searchresult=1">Expressivity</a></td>
              <td>Cosine Distance of topics to meaningless (stopword) embedding centroid</td>
            </tr>
            <tr>
              <td><a href="https://link.springer.com/chapter/10.1007/978-3-030-80599-9_4">Embedding Topic Diversity</a></td>
              <td>Topic diversity in the embedding space</td>
            </tr>
            <tr>
              <td><a href="https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00506/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality?searchresult=1">Embedding Coherence</a></td>
              <td>Cosine similarity between the centroid of the embeddings of the stopwords and the centroid of the topic.</td>
            </tr>
            <tr>
              <td><a href="https://aclanthology.org/E14-1056.pdf">NPMI</a></td>
              <td>Classical NPMi coherence computed on the source corpus.</td>
            </tr>
          </tbody>
        </table>
        </div>
        
        
        
        
        # 🗂️ Available Datasets
        To integrate custom datasets for modeling with STREAM, please follow the example notebook in the examples folder. For benchmarking new models, STREAM already includes the following datasets:
        <div align="center" style="width: 100%;">
          <table style="margin: 0 auto;">
          <thead>
            <tr>
              <th>Name</th>
              <th># Docs</th>
              <th># Words</th>
              <th># Features</th>
              <th>Description</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Spotify_most_popular</td>
              <td>5,860</td>
              <td>18,193</td>
              <td>17</td>
              <td>Spotify dataset comprised of popular song lyrics and various tabular features.</td>
            </tr>
            <tr>
              <td>Spotify_least_popular</td>
              <td>5,124</td>
              <td>20,168</td>
              <td>14</td>
              <td>Spotify dataset comprised of less popular song lyrics and various tabular features.</td>
            </tr>
            <tr>
              <td>Spotify</td>
              <td>11,012</td>
              <td>25,835</td>
              <td>14</td>
              <td>General Spotify dataset with song lyrics and various tabular features.</td>
            </tr>
            <tr>
              <td>Reddit_GME</td>
              <td>21,559</td>
              <td>11,724</td>
              <td>6</td>
              <td>Reddit dataset filtered for "Gamestop" (GME) from the Subreddit "r/wallstreetbets".</td>
            </tr>
            <tr>
              <td>Stocktwits_GME</td>
              <td>300,000</td>
              <td>14,707</td>
              <td>3</td>
              <td>Stocktwits dataset filtered for "Gamestop" (GME), covering the GME short squeeze of 2021.</td>
            </tr>
            <tr>
              <td>Stocktwits_GME_large</td>
              <td>600,000</td>
              <td>94,925</td>
              <td>0</td>
              <td>Larger Stocktwits dataset filtered for "Gamestop" (GME), covering the GME short squeeze of 2021.</td>
            </tr>
            <tr>
              <td>Reuters</td>
              <td>10,788</td>
              <td>19,696</td>
              <td>-</td>
              <td>Preprocessed Reuters dataset.</td>
            </tr>
            <tr>
              <td>Poliblogs</td>
              <td>13,246</td>
              <td>47,106</td>
              <td>2</td>
              <td>Preprocessed Poliblogs dataset suitable for STMs.</td>
            </tr>
            <tr>
              <td>20NewsGroups</td>
              <td>18,846</td>
              <td>70,461</td>
              <td>-</td>
              <td>preprocessed 20NewsGroups dataset</td>
            </tr>
            <tr>
              <td>BBC_News</td>
              <td>2,225</td>
              <td>19,116</td>
              <td>-</td>
              <td>preprocessed BBC News dataset</td>
            </tr>
          </tbody>
        </table>
        </div>
        If you wish yo include and publish one of your datasets directly into the package, feel free to contact us.
        
        
        
        # 🔧 Usage
        To use one of the available models, follow the simple steps below:
        1. Import the necessary modules:
        
            ```python
            from stream_topic.models import KmeansTM
            from stream_topic.utils import TMDataset
            ```
        ## 🛠️ Preprocessing
        2. Get your dataset and preprocess for your model:
            ```python
            dataset = TMDataset()
            dataset.fetch_dataset("20NewsGroup")
            dataset.preprocess(model_type="KmeansTM")
            ```
        
        The specified model_type is optional and further arguments can be specified. Default steps are predefined for all included models.
        Steps like stopword removal and lemmatizing are automatically performed for models like e.g. LDA.
        
        ## 🚀 Model fitting
        Fitting a model from STREAM follows a simple, sklearn-like logic and every model can be fit identically.
        
        3. Choose the model you want to use and train it:
           
            ```python
            model = KmeansTM()
            model.fit(dataset, n_topics=20)
            ```
        
        Depending on the model, check the documentation for hyperparameter settings. To get the topics, simply run:
        
        1. Get the topics:
            ```python
            topics = model.get_topics()
            ```
        
        ## ✅ Evaluation
        
        stream-topic implements various evaluation metrics, mostly focused around the intruder word task. The implemented metrics achieve high correlations with human evaluation. See [here](https://direct.mit.edu/coli/article/50/2/619/118990/Topics-in-the-Haystack-Enhancing-Topic-Quality) for the detailed description of the metrics.
        
        To evaluate your model simply use one of the metrics.
        ```python
        from stream_topic.metrics import ISIM, INT, ISH,Expressivity, NPMI
        
        metric = ISIM()
        metric.score(topics)
        ```
        
        Scores for each topic are available via:
        ```python
        metric.score_per_topic(topics)
        ```
        
        To leverage one of the metrics available in [octis](https://github.com/MIND-Lab/OCTIS), simply create a model output that fits within the octis' framework
        
        ```python
        from octis.evaluation_metrics.diversity_metrics import TopicDiversity
        
        model_output = {"topics": model.get_topics(), "topic-word-matrix": model.get_beta(), "topic-document-matrix": model.get_theta()}
        
        metric = TopicDiversity(topk=10) # Initialize metric
        topic_diversity_score = metric.score(model_output)
        
        ```
        
        Similarly to use one of STREAMS metrics for any model, use the topics and occasionally the $\beta$ (topic-word-matrix) of the model to calculate the score.
        
        ## 🔍 Hyperparameter optimization
        If you want to optimize the hyperparameters, simply run:
        ```python
        model.optimize_and_fit(
            dataset,
            min_topics=2,
            max_topics=20,
            criterion="aic",
            n_trials=20,
        )
        ```
        ## 🖼️ Visualization
        You can also specify to optimize with respect to any evaluation metric from stream_topic.
        Visualize the results:
        
        ```python
        from stream_topic.visuals import visualize_topic_model,visualize_topics
        visualize_topic_model(
            model, 
            reduce_first=True, 
            port=8051,
            )
        ```
        
        <p align="center">
            <img src="docs/images/gif1.gif" alt="Figure Description" width="750"/>
        </p>
        
        ## 📈 Downstream Tasks
        
        The general formulation of a Neural Additive Model (NAM) can be summarized by the equation:
        
        $$
        E(y) = h(β + ∑_{j=1}^{J} f_j(x_j)),
        $$
        
        where $h(·)$ denotes the activation function in the output layer, such as a linear activation for regression tasks or softmax for classification tasks. $x ∈ R^j$ represents the input features, and $β$ is the intercept. The function $f_j : R → R$ corresponds to the Multi-Layer Perceptron (MLP) for the $j$-th feature.
        
        Let's consider $x$ as a combination of categorical and numerical features $x_{tab}$ and document features $x_{doc}$. After applying a topic model, STREAM extracts topical prevalences from documents, effectively transforming the input into $z ≡ (x_{tab}, x_{top})$, a probability vector over documents and topics. Here, $x_{j(tab)}^{(i)}$ indicates the $j$-th tabular feature of the $i$-th observation, and $x_{k(top)}^{(i)}$ represents the $i$-th document's topical prevalence for topic $k$.
        
        For preserving interpretability, the downstream model is defined as:
        
        $$
        h(E[y]) = β + ∑_{j=1}^{J} f_j(x_{j(tab)}) + ∑_{k=1}^{K} f_k(x_{k(top)}),
        $$
        
        In this setup, visualizing the shape function `k` reveals the impact of a topic on the target variable `y`. For example, in the context of the Spotify dataset, this could illustrate how a topic influences a song's popularity.
        
        Fitting a downstream model with a pre-trained topic model is straightforward using the PyTorch Trainer class. Subsequently, visualizing all shape functions can be done similarly to the approach described by Agarwal et al. (2021).
        
        
        ```python
        from lightning import Trainer
        from stream_topic.NAM import DownstreamModel
        
        # Instantiate the DownstreamModel
        downstream_model = DownstreamModel(
            trained_topic_model=topic_model,
            target_column='target',  # Target variable
            task='regression',  # or 'classification'
            dataset=dataset,  
            batch_size=128,
            lr=0.0005
        )
        
        # Use PyTorch Lightning's Trainer to train and validate the model
        trainer = Trainer(max_epochs=10)
        trainer.fit(downstream_model)
        
        # Plotting
        from stream_topic.visuals import plot_downstream_model
        plot_downstream_model(downstream_model)
        ```
        
        # 🧪 Experimental 🧪
        stream-topic.experimental includes several experimental topic representations as well as new stuff we want to try out.
        
        This includes, e.g. topic summarization:
        
        ```python
        from stream_topic.experimental import stopic_summaries
        
        summaries = topic_summaries(topics, openai_key)
        for summary in summaries:
            print(f"{summary} \n")
        
        ```
        
        But also the possibility to generate a story from the created topics:
        
        ```python
        from stream_topic.experimental import story_topic
        
        story = story_topic(topics[1], openai_key)
        print(story)
        
        ```
        
        Lastly, it offers the possibility to visualize your topic in a way, a movie poster could be designed:
        
        ```python
        from stream_topic.experimental import movie_poster
        
        topic = ["tiger", "lion", "cougar", "cat", "hippo", "chair", "apple", "meat", "poachers", "hyeena"]
        
        movie_poster(topic, openai_key, return_style="plot")
        
        ```
        This is just one of many possible visualization, but we found that to be rather coherent in terms of truly visualizing the created topics.
        Feel free to contribute or rais issues fo further experimental ideas.
        
        <p align="center">
            <img src="assets/movie_poster_topic1.png" alt="Figure Description" width="600"/>
        </p>
        
        ## 🤝 Contributing and Testing New Models
        
        We welcome contributions! Before you start, please:
        
        1. **Check Existing Issues**: Look for existing issues or discussions that may cover your idea.
        2. **Fork and Clone**: Fork the repository and clone it to your local machine.
        3. **Create a Branch**: Work on a new branch to keep your changes organized.
        4. **Develop and Test**: Develop your model and validate it using our provided testing script.
        5. **Submit a Pull Request**: Once ready, submit a PR with a clear description of your changes.
        
        For detailed guidelines on how to structure your contributions, see below.ng instructions provided below.
        
        <h3> Steps for Contributing </h3>
        
        1. **Fork the Repository**:
           - Fork the repository to your GitHub account.
           - Clone the forked repository to your local machine.
           ```bash
           git clone https://github.com/your-username/your-repository.git
           cd your-repository
           ```
        
        2. **Create a New Branch**:
           - Ensure you are on the develop branch and create a new branch for your model development.
           ```bash
           git checkout develop
           git checkout -b new-model-branch
           ```
        
        3. **Develop Your Model**:
           - Navigate to the `mypackage/models/` directory.
           - Create your model class file, ensuring it follows the expected structure and naming conventions.
           - Implement the required methods (`get_info`, `fit`, `predict`) and attributes (`topic_dict`). Optionally, implement `beta`, `theta`, or corresponding methods (`get_beta`, `get_theta`).
        
        <h4> Example Model Structure </h4>
        
        Here is an example of how your model class should be structured:
        
        ```python
        import numpy as np
        from mypackage.models.abstract_helper_models.base import BaseModel, TrainingStatus
        
        class ExampleModel(BaseModel):
            def __init__(self, *args, **kwargs):
                super().__init__(*args, **kwargs)
                self._status = TrainingStatus.NOT_STARTED
        
            def get_info(self):
                return {"model_name": "ExampleModel", "trained": False}
        
            def any_other_processing_functions(self):
                pass
        
            def fit(self, dataset, n_topics=3):
                # do what you do during fitting the models
                self._status = TrainingStatus.INITIALIZED
                self._status = TrainingStatus.RUNNING
                self._status = TrainingStatus.SUCCEEDED
        
            def predict(self, texts):
                return [0] * len(texts)
        
            # If self.beta or self.theta are not assigned during fitting, plese include these two methods
            def get_beta(self):
                return self.beta
        
            def get_theta(self):
                return self.theta
        ```
        
        <h4> Testing Your Model </h4>
        
        1. **Install Dependencies**:
           - Ensure all dependencies are installed.
           ```bash
           pip install -r requirements.txt
           ```
        
        2. **Validate Your Model**:
           - To validate your model, use `tests/validate_new_model.py` to include your new model class.
           ```python
           from tests.model_validation import validate_model
        
           validate_model(NewModel)
           ```
        If this validation fails, it will tell you 
        
        <h4> Validation Criteria </h4>
        
        The following checks are performed during validation:
        - Presence of required methods (`get_info`, `fit`, `predict`).
        - Presence of required attributes (`topic_dict`).
        - Either presence of optional attributes (`beta`, `theta`) or corresponding methods (`get_beta`, `get_theta`).
        - Correct shape and sum of `theta`.
        - Proper status transitions during model fitting.
        - `get_info` method returns a dictionary with `model_name` and `trained` keys.
        
        Refer to the `tests/model_validation.py` script for detailed validation logic.
        
        <h4> Submitting Your Contribution </h4>
        
        1. **Commit Your Changes**:
           - Commit your changes to your branch.
           ```bash
           git add .
           git commit -m "Add new model: YourModelName"
           ```
        
        2. **Push to GitHub**:
           - Push your branch to your GitHub repository.
           ```bash
           git push origin new-model-branch
           ```
        
        3. **Create a Pull Request**:
           - Go to the original repository on GitHub.
           - Create a pull request from your forked repository and branch.
           - Provide a clear description of your changes and request a review.
        
        We appreciate your contributions and strive to make the integration process as smooth as possible. If you encounter any issues or have questions, feel free to open an issue on GitHub. Happy coding!
        
        If you want to include a new model where these guidelines are not approriate please mark this in your review request.
        
        
        
        # 📜 Citation
        
        If you use this project in your research, please consider citing:
        
        <h3> STREAM </h3>
        
        ```bibtex
        @inproceedings{thielmann-etal-2024-stream,
            title = {STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module},
            author = {Thielmann, Anton  and Reuter, Arik  and Weisser, Christoph  and Kant, Gillian  and Kumar, Manish  and S{\"a}fken, Benjamin},
            booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
            year = {2024},
            publisher = {Association for Computational Linguistics},
            pages = {435--444},
        }
        ```
        
        <h3> Metrics and CEDC </h3>
        
        ```bibtex
        @article{thielmann2024topics,
          title={Topics in the haystack: Enhancing topic quality through corpus expansion},
          author={Thielmann, Anton and Reuter, Arik and Seifert, Quentin and Bergherr, Elisabeth and S{\"a}fken, Benjamin},
          journal={Computational Linguistics},
          pages={1--37},
          year={2024},
          publisher={MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…}
        }
        ```
        
        <h3> TNTM </h3>
        
        ```bibtex
        @article{reuter2024probabilistic,
          title={Probabilistic Topic Modelling with Transformer Representations},
          author={Reuter, Arik and Thielmann, Anton and Weisser, Christoph and S{\"a}fken, Benjamin and Kneib, Thomas},
          journal={arXiv preprint arXiv:2403.03737},
          year={2024}
        }
        ```
        
        
        <h3> DCTE </h3>
        
        ```bibtex
        @inproceedings{thielmann2024human,
          title={Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class},
          author={Thielmann, Anton F and Weisser, Christoph and S{\"a}fken, Benjamin},
          booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
          pages={8395--8405},
          year={2024}
        }
        ```
        
        <h3> CBC </h3>
        
        ```bibtex
        @inproceedings{thielmann2023coherence,
          title={Coherence based document clustering},
          author={Thielmann, Anton and Weisser, Christoph and Kneib, Thomas and S{\"a}fken, Benjamin},
          booktitle={2023 IEEE 17th International Conference on Semantic Computing (ICSC)},
          pages={9--16},
          year={2023},
          organization={IEEE}
        
        ```
        
        If you use one of the Reddit or GME datasets, consider citing:
        ```bibtex
        @article{kant2024one,
          title={One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading},
          author={Kant, Gillian and Zhelyazkov, Ivan and Thielmann, Anton and Weisser, Christoph and Schlee, Michael and Ehrling, Christoph and S{\"a}fken, Benjamin and Kneib, Thomas},
          journal={Social Network Analysis and Mining},
          volume={14},
          number={1},
          pages={121},
          year={2024},
          publisher={Springer}
        }
        ```
        
        # 📝 License
        
        STREAM is released under the [MIT License](./LICENSE). © 2024