Comparison of Machine Learning Models to Predict Twitter Buzz
| Autor: | Eman Abdelfattah, Yash Parikh |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Marketing buzz
Computer science business.industry 02 engineering and technology Logistic regression Machine learning computer.software_genre Random forest Support vector machine ComputingMethodologies_PATTERNRECOGNITION Stochastic gradient descent 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Period (music) |
| Zdroj: | UEMCON |
| Popis: | This paper investigates six machine-learning models to determine which algorithm would effectively predict buzz on Twitter. Different classifiers are applied such as Stochastic Gradient Descent, Support Vector Machines, Logistic Regression, Deep Neural Networks, Random Forests and Extra Trees on a Twitter dataset. This dataset contains features with users and author engagement over a certain period. After tests conducted on all the algorithms, we concluded that Extra Trees model outperforms the other models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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