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Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages
2019
Digital Finance
We use a large dataset of one million messages sent on the microblogging platform StockTwits to evaluate the performance of a wide range of preprocessing methods and machine learning algorithms for sentiment analysis in finance. We find that adding bigrams and emojis significantly improve sentiment classification performance. However, more complex and time-consuming machine learning methods, such as random forests or neural networks, do not improve the accuracy of the classification. We also
doi:10.1007/s42521-019-00014-x
fatcat:olpp2fyntre75mlusm7k33geve