A Comparison of Multinomial Naïve Bayes and Bidirectional LSTM for Emotion Detection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50993-3_26
Reference17 articles.
1. Rajput, N., Ahuja, B., Rathi, V.: Word frequency and sentiment analysis of Twitter messages during Coronavirus pandemic (2020). https://doi.org/10.48550/arXiv.2004.03925
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3. Suneera, C., Prakash, J.: Performance analysis of machine learning and deep learning models for text classification, pp. 1–6 (2020). https://doi.org/10.1109/INDICON49873.2020.9342208
4. Tareq, A., Hewahi, N.: Sentiment analysis of tweets during COVID-19 pandemic using BLSTM. In: 2021 International Conference on Data Analytics for Business and Industry (ICDABI), Sakheer, Bahrain, pp. 245–249 (2021). https://doi.org/10.1109/ICDABI53623.2021.9655932
5. Vijay, T., Chawla, A., Dhanka, B., Karmakar, P.: Sentiment analysis on COVID-19 Twitter data. In: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, pp. 1–7 (2020). https://doi.org/10.1109/ICRAIE51050.2020.9358301
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