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