After having trained our BERTopic model, we can iteratively go through perhaps a hundred topic to get a good
understanding of the topics that were extract. 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, _ = topic_model.fit_transform(docs)Then, we simply call topic_model.visualize_topics() in order to visualize our topics. The resulting graph is a
plotly interactive graph which can be converted to html.
Thus, you can play around with the results below:
<iframe src="viz.html" style="width:1000px; height: 680px; border: 0px;""></iframe>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 size of the topic and its corresponding words.
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, _ = model.fit_transform(tweets)
topics_over_time = model.topics_over_time(tweets, topics, timestamps)Then, we visualize some interesting topics:
model.visualize_topics_over_time(topics_over_time, topcs=[9, 10, 72, 83, 87, 91])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, _ = topic_model.fit_transform(docs)
topics_per_class = topic_model.topics_per_class(docs, topics, classes=classes)Then, we visualize the topic representation of major topics per class:
topic_model.visualize_topics_per_class(topics_per_class)We can also calculate the probabilities of topics found in a document. In order to do so, we have to
set calculate_probabilities to True as calculating them can be quite computationally expensive.
Then, we use the variable probabilities that is returned from transform() or fit_transform()
to understand how confident BERTopic is that certain topics can be found in a document:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']
topic_model = BERTopic(calculate_probabilities=True)
topics, probabilities = topic_model.fit_transform(docs)To visualize the distributions, we simply call:
topic_model.visualize_distribution(probabilities[0])NOTE: The distribution of the probabilities does not give an indication to the distribution of the frequencies of topics across a document. It merely shows how confident BERTopic is that certain topics can be found in a document.