Topic_Modelling_Airlines_BERTopic
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("sneakykilli/Topic_Modelling_Airlines_BERTopic")
topic_model.get_topic_info()
Topic overview
- Number of topics: 17
- Number of training documents: 5134
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | killiair - flight - service - customer - airport | 23 | -1_killiair_flight_service_customer |
| 0 | killiair - doha - flight - service - worst | 2399 | poor_customer_experience |
| 1 | bag - luggage - cabin - bags - pay | 639 | luggage_fee |
| 2 | flight - delayed - hours - delay - killiair | 386 | delays |
| 3 | check - ryan - online - air - killiair | 334 | check_in_process |
| 4 | refund - killiair - flight - cancelled - booking | 293 | refund |
| 5 | jet - easy - flight - cancelled - refund | 237 | refund_cancelled_flights |
| 6 | seats - seat - plane - flight - killiair | 227 | inflight_facilities |
| 7 | luggage - lost - bag - killiair - baggage | 154 | luggage_lost |
| 8 | holiday - holidays - hotel - killiair - booked | 102 | hotel |
| 9 | thank - amazing - crew - flight - thanks | 81 | good_customer_experience |
| 10 | change - price - 115 - fare - booking | 59 | change_ticket_fee |
| 11 | food - meal - dubai - flight - killiair | 48 | inflight_service |
| 12 | car - hire - rental - insurance - card | 47 | car |
| 13 | seats - seat - paid - extra - window | 41 | seating_fees |
| 14 | service - killiair - customer - zero - customers | 37 | poor_customer_experience |
| 15 | stansted - flight - airport - parking - killiair | 27 | airport_facilities |
Training hyperparameters
- calculate_probabilities: False
- 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: False
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 1.24.3
- HDBSCAN: 0.8.33
- UMAP: 0.5.5
- Pandas: 2.0.3
- Scikit-Learn: 1.2.2
- Sentence-transformers: 2.3.1
- Transformers: 4.36.2
- Numba: 0.57.1
- Plotly: 5.16.1
- Python: 3.10.12
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