Summary of Machine Learning-based Nlp For Emotion Classification on a Cholera X Dataset, by Paul Jideani et al.
Machine Learning-based NLP for Emotion Classification on a Cholera X Dataset
by Paul Jideani, Aurona Gerber
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study aimed to investigate the emotions expressed in social media posts about cholera outbreaks, focusing on the classification of these emotions using machine learning models. Researchers extracted a dataset of 23,000 posts and applied natural language processing techniques to determine their emotional significance. Machine learning models including LSTM, logistic regression, decision trees, and BERT were used for emotion classification. The results showed that LSTM achieved an accuracy of 75%. This study demonstrates the potential of emotion classification in gaining insights into the impact of cholera on society, which could inform public health strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cholera outbreaks are big news on social media! People share all sorts of feelings about it – sadness, fear, worry. But nobody has really looked at what kind of emotions people express online about this disease. This study changed that by looking at 23,000 social media posts and using special computer tools to figure out how people felt. They used some fancy machine learning models to classify these emotions, like a big dictionary of feelings. The results showed that one type of model was really good at figuring out what people meant – it got 75% right! This study might help us understand how cholera affects society and how we can make better plans for keeping people healthy. |
Keywords
» Artificial intelligence » Bert » Classification » Logistic regression » Lstm » Machine learning » Natural language processing
