Topics

Visualizing BERTopic and its derivatives is important in understanding the model, how it works, and more importantly, where it works. Since topic modeling can be quite a subjective field it is difficult for users to validate their models. Looking at the topics and seeing if they make sense is an important factor in alleviating this issue.

Visualize Topics¶

After having trained our BERTopic model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis.

We embed our c-TF-IDF representation of the topics in 2D using Umap and then visualize the two dimensions using plotly such that we can create an interactive view.

First, we need to train our model:

from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups

docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)

Then, we can call .visualize_topics to create a 2D representation of your topics. The resulting graph is a plotly interactive graph which can be converted to HTML:

topic_model.visualize_topics()

You can use the slider to select the topic which then lights up red. If you hover over a topic, then general information is given about the topic, including the size of the topic and its corresponding words.

Visualize Topic Similarity¶

Having generated topic embeddings, through both c-TF-IDF and embeddings, we can create a similarity matrix by simply applying cosine similarities through those topic embeddings. The result will be a matrix indicating how similar certain topics are to each other. To visualize the heatmap, run the following:

topic_model.visualize_heatmap()

Note

You can set n_clusters in visualize_heatmap to order the topics by their similarity. This will result in blocks being formed in the heatmap indicating which clusters of topics are similar to each other. This step is very much recommended as it will make reading the heatmap easier.

Visualize Topics over Time¶

After creating topics over time with Dynamic Topic Modeling, we can visualize these topics by leveraging the interactive abilities of Plotly. Plotly allows us to show the frequency of topics over time whilst giving the option of hovering over the points to show the time-specific topic representations. Simply call .visualize_topics_over_time with the newly created topics over time:

import re
import pandas as pd
from bertopic import BERTopic

# Prepare data
trump = pd.read_csv('https://drive.google.com/uc?export=download&id=1xRKHaP-QwACMydlDnyFPEaFdtskJuBa6')
trump.text = trump.apply(lambda row: re.sub(r"http\S+", "", row.text).lower(), 1)
trump.text = trump.apply(lambda row: " ".join(filter(lambda x:x[0]!="@", row.text.split())), 1)
trump.text = trump.apply(lambda row: " ".join(re.sub("[^a-zA-Z]+", " ", row.text).split()), 1)
trump = trump.loc[(trump.isRetweet == "f") & (trump.text != ""), :]
timestamps = trump.date.to_list()
tweets = trump.text.to_list()

# Create topics over time
model = BERTopic(verbose=True)
topics, probs = model.fit_transform(tweets)
topics_over_time = model.topics_over_time(tweets, timestamps)

Then, we visualize some interesting topics:

model.visualize_topics_over_time(topics_over_time, topics=[9, 10, 72, 83, 87, 91])

Visualize Topics per Class¶

You might want to extract and visualize the topic representation per class. For example, if you have specific groups of users that might approach topics differently, then extracting them would help understanding how these users talk about certain topics. In other words, this is simply creating a topic representation for certain classes that you might have in your data.

First, we need to train our model:

from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups

# Prepare data and classes
data = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))
docs = data["data"]
classes = [data["target_names"][i] for i in data["target"]]

# Create topic model and calculate topics per class
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
topics_per_class = topic_model.topics_per_class(docs, classes=classes)

Then, we visualize the topic representation of major topics per class:

topic_model.visualize_topics_per_class(topics_per_class)