BERTopic | Leveraging BERT and c-TF-IDF to create | Topic Modeling library
kandi X-RAY | BERTopic Summary
kandi X-RAY | BERTopic Summary
For quick access to common functions, here is an overview of BERTopic's main methods:. | Method | Code | |-----------------------|---| | Fit the model | .fit(docs) | | Fit the model and predict documents | .fit_transform(docs) | | Predict new documents | .transform([new_doc]) | | Access single topic | .get_topic(topic=12) | | Access all topics | .get_topics() | | Get topic freq | .get_topic_freq() | | Get all topic information| .get_topic_info() | | Get representative docs per topic | .get_representative_docs() | | Get topics per class | .topics_per_class(docs, topics, classes) | | Dynamic Topic Modeling | .topics_over_time(docs, topics, timestamps) | | Update topic representation | .update_topics(docs, topics, n_gram_range=(1, 3)) | | Reduce nr of topics | .reduce_topics(docs, topics, nr_topics=30) | | Find topics | .find_topics("vehicle") | | Save model | .save("my_model") | | Load model | BERTopic.load("my_model") | | Get parameters | .get_params() |.
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Top functions reviewed by kandi - BETA
- Generate a graph showing the documents for a given topic model
- Returns the given topic
- Check that the topic model is fully fitted
- Extract embeddings from documents
- Visualize a topic model
- Calculate the annotation for each topic
- Returns the frequency of the topic
- Returns the topic representations of the model
- Embed a list of documents
- Plot topics over time
- Plot a Barchart histogram
- Visualize the distribution of the distribution
- Embeds a list of documents
- Plot the given topics
- Load a topic model
- Visualize the rank of the model
- Plot the heatmap
- Visualize a distribution
- Plot the hierarchy of the model
- Visualize documents
- Generate a figure of hierarchical documents
- Visualize topics over time
- Visualize the topics per class
- Generates a heat map for a topic model
- Generates a figure showing the term rank
- Generate a pandas dataframe showing the given documents
BERTopic Key Features
BERTopic Examples and Code Snippets
Lines of Code : 122License : Strong Copyleft (CC BY-SA 4.0)
Copyimport pandas as pd
from typing import List
import plotly.graph_objects as go
from sklearn.preprocessing import normalize
def visualize_topics_over_time(topic_model,
topics_over_time: pd.DataFrame,
Community Discussions
Trending Discussions on BERTopic
QUESTION
I have written my code to create a scatter plot with a color bar on the right. But the color bar does not look right, in the sense that the color is too light to be mapped to the actual color used in the plot. I am not sure what is missing or wrong here. But I am hoping to get something similar to what's shown here: https://medium.com/@juliansteam/what-bert-topic-modelling-reveal-about-the-2021-unrest-in-south-africa-d0d15629a9b4 (about in the middle of the page)
...ANSWER
Answered 2022-Mar-24 at 22:20The colorbar uses the given alpha=.3. In the scatterplot, many dots with the same color are superimposed, causing them to look brighter than a single dot.
One way to tackle this, is to create a ScalarMappable object to be used by the colorbar, taking the colormap and the norm of the scatter plot (but not its alpha). Note that simply changing the alpha of the scatter object (scatter.set_alpha(1)) would also change the plot itself.
QUESTION
I am using BERTopic to perform the topic modelling, everything works perfectly fine. However, since I am forcing the algorithm to give me 10 topics using nr_topics=10 as output, and when I visualize the topics overtime using
topic_model.visualize_topics_over_time(topics_over_time, top_n_topics=10, width=1250, height=450), some colors are repeated for topics as there are only 7 colors mentioned in the function visualize_topics_over_time. I tried executing the same function in my python notebook with additional color values, but it gives me the following error:
Can someone please help me update the function with additional four colors?
...ANSWER
Answered 2022-Feb-08 at 08:25To add colors to the function, you will indeed have to copy the function and change it to include more colors:
QUESTION
For Topic Modelling, I'm trying out the BERTopic: Link
I'm little confused here, I am trying out the BERTopic on my custom Dataset.
Since BERT was trained in such a way that it holds the semantic meaning of the text/document,
Should I be removing the stop words and stem/lemmatize my documents before passing it onto BERTopic?
Because I'm afraid if these stopwords might land into my topics as salient terms which they are not
Suggestions and Advices please!
...ANSWER
Answered 2021-Jun-25 at 08:49A good way to know if this is needed is to check the examples/tutorials given by the link you provided : Here is Topic Modeling. As you can see, it does not seem to do any preprocess before calling the model.
It then seems that it's not needed or preconised by the authors of the model.
However, removing stopwords can make the whole process faster and they often do not contains salient informations about the topic (by their nature). It is sometimes preconised not to remove them for certains tasks such as Sentiment Analysis as you can read in these links :
Why is removing stopwords not always a good idea ?
DataStack discussion over stopwords
As for Lemmatization or Stemmatization, this link provides you good insights about the subject for a Topic Modeling task saying that it should be implemented for improved results.
In conclusion, the BERTTopic does not need Lemming/stemming nor removing stopwords to work but can be implemented to enhance both processing time and results. At the end, it always depend on your needs and ressources. Giving a try to both solutions and compare the results you have depending on what you want is always a good way to understand pros and cons about these tools.
QUESTION
I am trying to install bertopic[visualization] in my macbook pro using
...ANSWER
Answered 2021-Jan-08 at 05:37zsh uses square brackets for pattern matching which means that if you need to pass literal square brackets as an argument to a command, you either need to escape them or quote the argument like this:
so try using:
pip3 install 'bertopic[visualization]'
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install BERTopic
For an in-depth overview of the features of BERTopic you can check the full documentation here or you can follow along with one of the examples below:.
We start by extracting topics from the well-known 20 newsgroups dataset containing English documents:.
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