TopicModel_StoreReviews
This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
Usage
To use this model, please install BERTopic:
pip install -U bertopic
You can use the model as follows:
from bertopic import BERTopic
topic_model = BERTopic.load("shantanudave/TopicModel_StoreReviews")
topic_model.get_topic_info()
Topic overview
- Number of topics: 10
- Number of training documents: 14747
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| 0 | clothing - clothes - fashion - clothe - clothing store | 2672 | Fashionable Clothing Selection |
| 1 | shopping - shop - price - cheap - store | 1864 | Diverse Shopping Experiences |
| 2 | tidy - clean - branch - range - renovation | 1807 | Clean Retail Space |
| 3 | quality - offer - use - stop - good | 1793 | Quality Offer Search |
| 4 | selection - choice - large - large selection - size | 1459 | Large Size Selection |
| 5 | advice - saleswoman - service - friendly - competent | 1447 | Friendly Saleswoman Service |
| 6 | staff - friendly staff - staff staff - staff friendly - friendly | 1177 | Friendly Staff Selection |
| 7 | wow - waw - oh - yeah - | 1108 | Expressive Words Discovery |
| 8 | voucher - money - return - exchange - cash | 933 | Customer Return Experience |
| 9 | super - friendly super - super friendly - pleasure - super service | 487 | super friendly service |
Training hyperparameters
- calculate_probabilities: True
- language: None
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: None
- seed_topic_list: None
- top_n_words: 10
- verbose: True
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 1.23.5
- HDBSCAN: 0.8.33
- UMAP: 0.5.5
- Pandas: 1.3.5
- Scikit-Learn: 1.4.1.post1
- Sentence-transformers: 2.6.1
- Transformers: 4.39.3
- Numba: 0.59.1
- Plotly: 5.21.0
- Python: 3.10.13
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