Twitter is a robust medium to understand wide-scale, organic public perception about the COVID-19 vaccine . In this cross-sectional observational study, we evaluated 2.4 million English tweets from nearly 1 million user accounts matching keywords (( 'covid *' OR 'coronavirus') AND 'vaccine') during vaccine development from Feb 1st through Dec 11th , 2020 . We applied topic modeling, sentiment and emotion analysis, and demographic inference of users on the COVID-19 vaccine related tweets to provide insight into the evolution of public attitudes . Individuals generated 87.9% (n=834,224) of tweets . Of individuals, men (n=560,824) outnumbered women (n=273,400) by 2:1 and 39.5% (n=329,776) of individuals were [≥] 40 years old . Daily mean sentiment fluctuated congruent with news events, but overall trended positively . Trust, anticipation, and fear were the three most predominant emotions; while fear was the most predominant emotion early in the study period, trust outpaced fear from April 2020 onward . Fear was more prevalent in tweets by individuals (26.3% vs. organizations 19.4% ; p <0.001), specifically among women (28.4% vs. males 25.4% ; p <0.001). Multiple topics had a monthly trend towards more positive sentiment . Tweets comparing COVID-19 to the influenza vaccine had strongly negative early sentiment but improved over time . Our findings are concerning for COVID-19 vaccine hesitancy, but also identify targets for educational interventions.