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- W4316658621 abstract "In the era of social media, it's easier for people to express themselves. Every day, they share their views and ideas on numerous social media platforms about current events across the world. On hot issues, one can get a general sense of public opinion, whether favorable or unfavorable. Twitter is an example of a global social media platform where people discuss their opinions. By identifying these sentiments, machine learning provides us an advantage in analyzing and predicting them. Different machine learning models are employed to analyze sentiments in Twitter data in this study. Sentiment140, a Twitter dataset that is divided into positive and negative tweets, is used for training. The performance of several models such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), as well as their variants, are compared in this Dataset." @default.
- W4316658621 created "2023-01-17" @default.
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- W4316658621 date "2022-11-24" @default.
- W4316658621 modified "2023-09-25" @default.
- W4316658621 title "Comparison of Machine Learning Algorithms for Twitter Sentiment Analysis" @default.
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- W4316658621 doi "https://doi.org/10.1109/icaiss55157.2022.10010781" @default.
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