Add BERTopic.

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_`.