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Twitter Based Sentiment Analysis of Each Presidential Candidate Using Long Short-Term Memory
Dhruval Shah, Yanyan Li, Ahmad Hadaegh
Pages - 87 - 96     |    Revised - 31-05-2021     |    Published - 30-06-2021
Volume - 15   Issue - 3    |    Publication Date - June 2021  Table of Contents
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KEYWORDS
Sentiment Analysis, LSTM, Deep Learning, Natural Language Processing, Data Mining.
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.
1 Google Scholar 
2 Semantic Scholar 
3 refSeek 
4 BibSonomy 
5 ResearchGate 
6 Doc Player 
7 J-Gate 
8 Scribd 
9 SlideShare 
A novel classification approach based on Naïve Bayes for Twitter sentiment analysis. (2017). KSII Transactions on Internet and Information Systems, 11 (6). https://doi.org/10.3837/tiis.2017.06.011.
A. Tumasjan, T. Sprenger, P. Sandner, and I. Welpe. Predicting elections with Twitter: What 140 characters reveal about political sentiment, Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), pp. 178–185, 2010.
Aggarwal, Raghav, Bi-LSTM, published in 4 July 2019, medium.com/@raghavaggarwal0089/bi-lstm-bc3d68da8bd0.
Alloghani M., Al-Jumeily D., Mustafina J., Hussain A., Aljaaf A.J. (2020) A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In: Berry M., Mohamed A., Yap B. (eds) Supervised and Unsupervised Learning for Data Science. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_1.
B. Joyce and J. Deng, "Sentiment analysis of tweets for the 2016 US presidential election," 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, 2017, pp. 1-4, doi: 10.1109/URTC.2017.8284176.
Gautam, G., & Yadav, D. (2014). Sentiment analysis of twitter data using machine learning approaches and semantic analysis. 2014 Seventh International Conference on Contemporary Computing (IC3). doi:10.1109/ic3.2014.6897213.
Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Keim, D. A., Haug, L.-E., & Hsu, M.-C. (2011). Visual sentiment analysis on twitter data streams. 2011 IEEE Conference on Visual Analytics Science and Technology (VAST). doi:10.1109/vast.2011.6102472.
How Democrats and Republicans Use Twitter, Pew Research Center, published in Oct 15, 2020, https://www.pewresearch.org/politics/2020/10/15/differences-in-how-democrats-and-republicans-behave-on-twitter/ accessed: 2021-04-15.
Jefferson Henrique, Get Old Tweet in Python, https://github.com/Jefferson-Henrique/GetOldTweets-python, accessed: 2020-08-08.
Khuong, Ben. RNN Definition. The Basics of Recurrent Neural Networks (RNNs). 11 June 2020, medium.com/towards-artificial-intelligence/whirlwind-tour-of-rnns-a11effb7808f.
Khuong, Ben. RNN Figure. The Basics of Recurrent Neural Networks (RNNs). 11 June 2020, medium.com/towards-artificial-intelligence/whirlwind-tour-of-rnns-a11effb7808f.
Ladkin, D. (2017). How did that happen? Making sense of the 2016 US presidential election result through the lens of the ‘leadership moment.’ Leadership, 13(4), 393–412.
Long Short-Term Memory, https://en.wikipedia.org/wiki/Long_short-term_memory/.
P. Sharma and T. Moh, "Prediction of Indian election using sentiment analysis on Hindi Twitter," 2016 IEEE International Conference on Big Data (IEEE Big Data 2016), 2016, pp. 1966-1971, doi: 10.1109/BigData.2016.7840818.
Social Media on the Campaign Trail: Barack Obama and Donald Trump, accessed on August 8 2020, https://contentgroup.com.au/2017/09/social-media-campaign-trail-obama-trump/.
Tang Y. and Liu J., Gated Recurrent Units for Airline Sentiment Analysis of Twitter Data, Technical Report, Stanford University, 2011.
Twitter Usage Statistics, https://www.internetlivestats.com/twitter-statistics/, accessed: 2020-08-08.
Vader Sentiment API, https://pypi.org/project/vaderSentiment/.
What Is Model Training, https://oden.io/glossary/model-training/, accessed: 2020-08-08.
Mr. Dhruval Shah
Computer Science and Information Systems, California State University San Marcos, San Marcos, CA, 92096 - United States of America
Dr. Yanyan Li
Computer Science and Information Systems, California State University San Marcos, San Marcos, CA, 92096 - United States of America
yali@csusm.edu
Dr. Ahmad Hadaegh
Computer Science and Information Systems, California State University San Marcos, San Marcos, CA, 92096 - United States of America


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