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Add BERTopic.

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LLMTopicDetection_BERTopic/docs/getting_started/multiaspect/multiaspect.md
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During the development of BERTopic, many different types of representations can be created, from keywords and phrases to summaries and custom labels. There is a variety of techniques that one can choose from to represent a topic. As such, there are a number of interesting and creative ways one can summarize topics. A topic is more than just a single representation.
Therefore, `multi-aspect topic modeling` is introduced! During the `.fit` or `.fit_transform` stages, you can now get multiple representations of a single topic. In practice, it works by generating and storing all kinds of different topic representations (see image below).
<figure markdown>
![Image title](multiaspect.svg)
<figcaption></figcaption>
</figure>
The approach is rather straightforward. We might want to represent our topics using a `PartOfSpeech` representation model but we might also want to try out `KeyBERTInspired` and compare those representation models. We can do this as follows:
```python
from bertopic.representation import KeyBERTInspired
from bertopic.representation import PartOfSpeech
from bertopic.representation import MaximalMarginalRelevance
from sklearn.datasets import fetch_20newsgroups
# Documents to train on
docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']
# The main representation of a topic
main_representation = KeyBERTInspired()
# Additional ways of representing a topic
aspect_model1 = PartOfSpeech("en_core_web_sm")
aspect_model2 = [KeyBERTInspired(top_n_words=30), MaximalMarginalRelevance(diversity=.5)]
# Add all models together to be run in a single `fit`
representation_model = {
"Main": main_representation,
"Aspect1": aspect_model1,
"Aspect2": aspect_model2
}
topic_model = BERTopic(representation_model=representation_model).fit(docs)
```
As show above, to perform multi-aspect topic modeling, we make sure that `representation_model` is a dictionary where each representation model pipeline is defined.
The main pipeline, that is used in most visualization options, is defined with the `"Main"` key. All other aspects can be defined however you want. In the example above, the two additional aspects that we are interested in are defined as `"Aspect1"` and `"Aspect2"`.
After we have fitted our model, we can access all representations with `topic_model.get_topic_info()`:
<br><br>
<img src="table.PNG">
<br><br>
As you can see, there are a number of different representations for our topics that we can inspect. All aspects are found in `topic_model.topic_aspects_`.
LLMTopicDetection_BERTopic/docs/getting_started/multiaspect/multiaspect.svg
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