text: Title: Topics_over_time() labels represent different topics at different points in time
  Body: Hello @MaartenGr,
  I've been loving this package so far! It's been extremely useful.
  
  I have an inquiry regarding unexpected behavior in output from topics_over_time(). I've included code and output below but I will briefly contextualize the problem in words. I am using textual data from the Reuters Newswire from the year 2020. I use online topic modeling and monthly batches of the data to update my topic model. After this, I run topics_over_time() on the entire sample and use the months as my timestamps. All this works well. However, some of the same labels in topics_over_time() seem to represent vastly different topics in different points of time (the images below focus on label 18 as an example). It was my understanding that the label should represent the same overall topic over time, with the keywords changing based on how the corpus discusses the topic. However,  the topic entirely shifts from the Iran nuclear deal to COVID-19. 
  
  Is there a way to prevent this from happening? It seems likely I've made some error in logic in my code (which I've included below).
  
  Thanks so much in advance!
  
  ```python
  data = pd.read_csv("/tr/proj15/txt_factors/Topic Linkage Experiments/Pull Reuters Data/Output/2020_textual_data.csv")
  
  #Separate text data to generate topics over time
  whole_text_data = data["body"]
  whole_text_data = whole_text_data.replace('\n', ' ')
  whole_text_data.reset_index(inplace = True, drop = True)
  
  date_col = data["month_date"].to_list()
  
  unique_dates_df = data.drop_duplicates(subset=['month_date'])
  timestamps = unique_dates_df["month_date"].to_list()
  
  #Set up parameters for Bertopic model
  model = BertForSequenceClassification.from_pretrained('ProsusAI/finbert')
  cluster_model = River(cluster.DBSTREAM())
  vectorizer_model = OnlineCountVectorizer(stop_words="english", ngram_range=(1,4))
  ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True, bm25_weighting=True)
  
  umap_model = UMAP(n_neighbors=25, 
                              n_components=10, 
                              metric='cosine')
  
  topic_model = BERTopic(
      umap_model = umap_model,
      hdbscan_model=cluster_model, 
      vectorizer_model=vectorizer_model, 
      ctfidf_model=ctfidf_model,
      nr_topics = "auto"
  )
  
  #Incrementally learn
  topics = []
  for month in timestamps:
      month_df = data.loc[data['month_date'] == month]
      text_data = month_df["body"]
      text_data = text_data.replace('\n', ' ')
      text_data.reset_index(inplace = True, drop = True)
      topic_model.partial_fit(text_data)
      topics.extend(topic_model.topics_)
  
  topic_model.topics_ = topics
  
  topics_over_time = topic_model.topics_over_time(whole_text_data, date_col, 
                                                  datetime_format="%Y-%m",
                                                  global_tuning = True,
                                                  evolution_tuning = True)
  
  topics_over_time.to_csv('2020_topics_over_time.csv', index = False)
  ```
  
  ![2020_January_topic_18](https://github.com/MaartenGr/BERTopic/assets/97412806/f3c67285-9dfe-49bc-bc61-9e31ce741aa2)
  ![2020_March_topic_18](https://github.com/MaartenGr/BERTopic/assets/97412806/440fae0d-acfb-4096-8dc8-ed1499f933e1)
  