BERTopic
PyABSA
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| BERTopic | PyABSA | |
|---|---|---|
| 22 | 2 | |
| 5,543 | 858 | |
| - | - | |
| 8.2 | 5.3 | |
| 8 days ago | 6 days ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
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BERTopic
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how can a top2vec output be improved
Try experimenting with different hyperparameters, clustering algorithms and embedding representations. Try https://github.com/MaartenGr/BERTopic/tree/master/bertopic
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SBERT Embeddings from Conversations
Try out this notebook which comes with the BERTopic repository.
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Sentence transformers (BERTopic) on a Macbook Air
After some googling, I found this (but for M1 chip Mac) --I wonder if I'm stuck. Is this laptop just not up for the job of working with sentence transformers? Appreciate your advice
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Comparing BERTopic to human raters
Most has already been said and I am not sure how relevant this is but since you are focusing on human raters it might be worthwhile to mention that there is a Pull Request in BERTopic that allows you to use models on top of the default pipeline that further fine-tunes the topic representation. In theory, this would allow you to even use ChatGPT or any of the other OpenAI models to label the topics. From a human annotator perspective, this might be interesting to pursue.
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text clustering with XLNET, ROBERTA, ELMO and other pretrained models
The BERTopic library allows you to plug and play any type of embedding.
- How can I group domain specific keywords based on their word embeddings?
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Introducing the Semantic Graph
A number of excellent topic modeling libraries exist in Python today. BERTopic and Top2Vec are two of the most popular. Both use sentence-transformers to encode data into vectors, UMAP for dimensionality reduction and HDBSCAN to cluster nodes.
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Classifying unstructured text: sentences, phrases, lists of words
BERTopic is a library to consider if you want something that groups data by topic.
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[D] How to best extract product benefits/problems from customer reviews using NLP?
I have experimented a bit with BERTopic but didn't find the results very useful. The issue is, that it is very important what exactly people are liking or disliking about the products, not just the fact that they are talking about specific aspects.
- Classify texts using known categories, NLP
PyABSA
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Extracting targeted sentiment from from product reviews
Check out: https://github.com/yangheng95/PyABSA
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[D] How to best extract product benefits/problems from customer reviews using NLP?
https://github.com/yangheng95/PyABSA - for extraction of aspects + corresponding sentiments
What are some alternatives?
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
MAPIE - A scikit-learn-compatible module for estimating prediction intervals.
gensim - Topic Modelling for Humans
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
SelSum - Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.
GuidedLDA - semi supervised guided topic model with custom guidedLDA
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
contextualized-topic-models - A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
100DaysofMLCode - My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:
scattertext - Beautiful visualizations of how language differs among document types.
glasgow-litter - A project that explores the relationship between deprivation and litter in Glasgow City. 🚯