%0 Journal Article %1 shahtwitter %A Shah, Dhruval %A Li, Yanyan %A Hadaegh, Ahmad %D 2021 %J International Journal of Computer Science and Security (IJCSS) %K Analysis, Data Deep LSTM, Language Learning, Mining Natural Processing, Sentiment %N 3 %P 87-96 %T Twitter Based Sentiment Analysis of Each Presidential Candidate Using Long Short-Term Memory %U https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJCSS-1617 %V 15 %X In the era of technology and internet, people use online social media services like Twitter, Instagram, Facebook, Reddit, etc. to express their emotions. The idea behind this paper is to understand people’s emotion on Twitter and their opinion towards Presidential Election 2020. We collected 1.2 million tweets in total with keyword like “RealDonaldTrump”, “JoeBiden”, “Election2020” and other election related keywords using Twitter API and then processed them with natural language processing toolkit. A Bidirectional Long Short-Term Memory (BiLSTM) model has been trained and we have achieved 93.45% accuracy on our test dataset. We then used our trained model to perform sentiment analysis on the rest of our dataset. With the sentiment analysis results and comparison with 2016 Presidential Election, we have made predictions on who could win the US Presidential Election in 2020 with pre-election twitter data. We have also analyzed the impact of COVID-19 on people’s sentiment about the election.

@article{shahtwitter, abstract = {In the era of technology and internet, people use online social media services like Twitter, Instagram, Facebook, Reddit, etc. to express their emotions. The idea behind this paper is to understand people’s emotion on Twitter and their opinion towards Presidential Election 2020. We collected 1.2 million tweets in total with keyword like “RealDonaldTrump”, “JoeBiden”, “Election2020” and other election related keywords using Twitter API and then processed them with natural language processing toolkit. A Bidirectional Long Short-Term Memory (BiLSTM) model has been trained and we have achieved 93.45% accuracy on our test dataset. We then used our trained model to perform sentiment analysis on the rest of our dataset. With the sentiment analysis results and comparison with 2016 Presidential Election, we have made predictions on who could win the US Presidential Election in 2020 with pre-election twitter data. We have also analyzed the impact of COVID-19 on people’s sentiment about the election.}, added-at = {2021-08-26T08:47:45.000+0200}, author = {Shah, Dhruval and Li, Yanyan and Hadaegh, Ahmad}, biburl = {https://www.bibsonomy.org/bibtex/28addf8357fd07ba2d6293f148c1e263b/cscjournals}, interhash = {1b3dec5d691d652b48d93ecac0c57976}, intrahash = {8addf8357fd07ba2d6293f148c1e263b}, issn = {1985-1553}, journal = {International Journal of Computer Science and Security (IJCSS)}, keywords = {Analysis, Data Deep LSTM, Language Learning, Mining Natural Processing, Sentiment}, language = {English}, month = {June}, number = 3, pages = {87-96}, timestamp = {2021-08-26T08:47:45.000+0200}, title = {Twitter Based Sentiment Analysis of Each Presidential Candidate Using Long Short-Term Memory}, url = {https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJCSS-1617}, volume = 15, year = 2021 }