Add BERTopic.

LLMTopicDetection_BERTopic/docs/getting_started/topicsperclass/topicsperclass.md

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+In some cases, you might be interested in how certain topics are represented over certain categories. Perhaps
+there are specific groups of users for which you want to see how they talk about certain topics.
+
+Instead of running the topic model per class, we can simply create a topic model and then extract, for each topic, its representation per class. This allows you to see how certain topics, calculated over all documents, are represented for certain subgroups.
+
+<br>
+<div class="svg_image">
+--8<-- "docs/getting_started/topicsperclass/class_modeling.svg"
+</div>
+<br>
+
+
+To do so, we use the 20 Newsgroups dataset to see how the topics that we uncover are represented in the 20 categories of documents.
+
+First, let's prepare the data:
+
+```python
+from bertopic import BERTopic
+from sklearn.datasets import fetch_20newsgroups
+
+data = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))
+docs = data["data"]
+targets = data["target"]
+target_names = data["target_names"]
+classes = [data["target_names"][i] for i in data["target"]]
+```
+
+Next, we want to extract the topics across all documents without taking the categories into account:
+
+```python
+topic_model = BERTopic(verbose=True)
+topics, probs = topic_model.fit_transform(docs)
+```
+
+Now that we have created our global topic model, let us calculate the topic representations across each category:
+
+```python
+topics_per_class = topic_model.topics_per_class(docs, classes=classes)
+```
+
+The `classes` variable contains the class for each document. Then, we simply visualize these topics per class:
+
+```python
+topic_model.visualize_topics_per_class(topics_per_class, top_n_topics=10)
+```
+<iframe src="topics_per_class.html" style="width:1000px; height: 1100px; border: 0px;""></iframe>
+
+You can hover over the bars to see the topic representation per class.
+
+As you can see in the visualization above, the topics `93_homosexual_homosexuality_sex` and `58_bike_bikes_motorcycle`
+are somewhat distributed over all classes.
+
+You can see that the topic representation between rec.motorcycles and rec.autos in `58_bike_bikes_motorcycle` clearly
+differs from one another. It seems that BERTopic has tried to combine those two categories into a single topic. However,
+since they do contain two separate topics, the topic representation in those two categories differs.
+
+We see something similar for `93_homosexual_homosexuality_sex`, where the topic is distributed among several categories
+and is represented slightly differently.
+
+Thus, you can see that although in certain categories the topic is similar, the way the topic is represented can differ.