PyCaret
PulseAugur coverage of PyCaret — every cluster mentioning PyCaret across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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LLM-agent framework DyRIS improves space group prediction for imbalanced perovskite data
Researchers have developed DyRIS, a novel LLM-agent framework designed to predict the space groups of double perovskites, a class of materials with significant catalytic potential. Existing datasets for this task are of…
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XGBoost algorithm predicts e-commerce customer satisfaction from YouTube comments
This research paper introduces a predictive model for customer satisfaction using the XGBoost algorithm and TF-IDF vectorization on YouTube comments from Indonesian e-commerce review videos. The study found that the PyC…
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CNN-BiLSTM outperforms AutoML for Indonesian Twitter hate speech detection
This paper compares PyCaret AutoML and a CNN-BiLSTM model for detecting hate speech on Indonesian Twitter. The CNN-BiLSTM model achieved superior performance, with an accuracy of 83.8% and an F1-score of 81.2%, outperfo…
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Indonesian sentiment analysis: ML models outperform deep learning on reviews
Two recent papers benchmark traditional machine learning models against deep learning approaches for sentiment analysis on Indonesian text data. One study on Tokopedia reviews found that a Linear SVC model outperformed …
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LSTM deep learning model outperforms ML for Mobile Legends app review sentiment analysis
This paper evaluates machine learning and LSTM-based deep learning models for sentiment analysis of Mobile Legends app reviews. Utilizing a dataset of 10,000 labeled reviews, the study found that the LSTM model achieved…
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Researchers use BiLSTM with attention to improve game review sentiment analysis
Researchers have developed an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model to improve sentiment classification of Steam game reviews. This deep learning approach, implemented in PyTorch, was train…
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Studies benchmark AutoML and BiLSTM for NLP tasks, showing mixed results
Researchers have compared traditional machine learning methods with deep learning models for various natural language processing tasks, including fine-grained emotion classification and sentiment analysis. Studies utili…