Abstract
Tweet sentiment analysis has been an effective and valuable technique in the sentiment analysis domain. We conduct a systematic and thorough empirical study on traditional machine learning algorithms and two deep learning approaches for tweet sentiment analysis, and expect to provide a guideline for choosing which efficient classification algorithms. Based on our experiments, we found that the Support Vector Machine and the Random Forest work better statistically than other methods. Although deep learning approaches have achieved many successes in image and voice processing, simple RNN and LSTM networks do not outweigh SVM and RF in our experiments. Moreover, for the tweet feature selection, the combination of bi-grams, SentiWordNet and Stop words removal shows more effectiveness in accuracy improving.
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Acknowledgements
This work is supported by the NSFC (61272421, 41271410), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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Yan, L., Tao, H. (2016). An Empirical Study and Comparison for Tweet Sentiment Analysis. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_55
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DOI: https://doi.org/10.1007/978-3-319-48674-1_55
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