  One major sub-domain in the subject of polling public opinion with social
media data is electoral prediction. Electoral prediction utilizing social media
data potentially would significantly affect campaign strategies, complementing
traditional polling methods and providing cheaper polling in real-time. First,
this paper explores past successful methods from research for analysis and
prediction of the 2020 US Presidential Election using Twitter data. Then, this
research proposes a new method for electoral prediction which combines
sentiment, from NLP on the text of tweets, and structural data with aggregate
polling, a time series analysis, and a special focus on Twitter users critical
to the election. Though this method performed worse than its baseline of
polling predictions, it is inconclusive whether this is an accurate method for
predicting elections due to scarcity of data. More research and more data are
needed to accurately measure this method's overall effectiveness.
