Sentiment is all you need to win US Presidential elections (2022)

Abstract: Election speeches play an integral role in communicating the vision and mission of the candidates. From lofty promises to mud-slinging, the electoral candidate accounts for all. However, there remains an open question about what exactly wins over the voters. In this work, we used state-of-the-art natural language processing methods to study the speeches and sentiments of the Republican candidates and Democratic candidates fighting for the 2020 US Presidential election. Comparing the racial dichotomy of the United States, we analyze what led to the victory and defeat of the different candidates. We believe this work will inform the election campaigning strategy and provide a basis for communicating to diverse crowds.

## introduction in a democracy, elections serve as the people's mandate. they speak for what the people think and who they want to represent their voices. however, the mandate is not without bias and is often swayed by the communication at election rallies, on social media, and at dinner table conversations @xcite . understanding what reinforces people's opinions, or changes them, is a complex question @xcite . election campaign of a candidate attempts to decode what the people want and focuses the messaging around that @xcite . the audience strategy is usually based on heuristics like the assumption that voters vote based on their economic interests leads them to change the economy for the better, for instance, by introducing support funds or creating new jobs, especially shortly before an election @xcite . these are also being dominated by people who happen to know the intricacies of the ground @xcite . with the rise in usage of post hoc analysis of strategies, elections have started to become less heuristic-driven and more data-driven @xcite . one of the most prominent and mobilizing parts of the elections are the speeches, and the use of language, by the running candidates @xcite . the sentiments and statements in speeches are often described as an art. in this paper, we attempted to decode the art of speeches by focusing on the sentiment classification of speeches across various states and demographics, and how their effect on the election results. a survey was also conducted to analyze people's responses to snippets of the speeches. ## related work in the social sciences field, there have been multiple approaches to analyze the us presidential election speeches. populism framing in the speeches has been explored to analyze speeches in a novel database comprising of speeches from 1896 to 2016 in fahey (2021), and a more recent focus with active metaphors in @xcite . political rhetoric @xcite , deception strategies @xcite , and metrical analysis @xcite , amongst other linguistic approaches have been used to study presidential elections for different years. however, these approaches do not leverage the significant advances in artificial intelligence to push forth their analyses. the rise of sentimental analysis and widespread availability of twitter data have contributed to more computational analysis in the recent years. several studies analyzed the twitter responses, tweets by donald trump, and their aftermath using nlp tools @xcite @xcite . political sentiment anal-ysis and the use of sentiment analysis to predict election results has been attempted @xcite @xcite . @xcite provide a text analysis of the 2020 us election speeches. we believe that the extension of these approaches to more recent emnotion-based approaches, along with human surveys could provide a robust method of understanding the effect that the speeches have. in 2021, goemotions, a database of fine-grained emotions, labeled for 27 emotion categories was released by a team of researchers from google, amazon and stanford linguistics department @xcite . sequence to emotion models were developed in @xcite , visualization of the emotions was done in dumont and facen, and these models were applied to text sentiment analysis and essay analysis @xcite . more advanced models, such as emoroberta, have been recently developed @xcite , and limitations of the text-based emotion detection have also been discussed @xcite . ## limitations, and future work the small lexicon considering just speeches targeting the 2020 presidential elections results in detecting a relatively narrow understanding of how the sentiments used in the speeches affect the individuals. furthermore, the idea behind voting for an individual comes with many prejudices against the candidate and the party representing. this was observed in the survey when we put forward the same snippet and attributed it to a different speaker. in our study, we nevertheless saw an interesting set of emotions that have driven the 2020 presidential elections for either party, such as section 4.6, where we noted how positivity and negativity notions had impacts when used by either party, and section 4.4 and 4.5, where we noted that when the parties used particular emotion-based sentences, they had to face a loss. future work, including understanding the impact of emotions on voters from different backgrounds, such as immigrants, white-and blue-collar workers, and other demographics, can shed light on the relationship between how the particular emotion-based sentences can sway the elections. considering multiple years of presidential elections rally speeches and understanding the opinions biased by the individual's background would be vital in understanding the changing landscape of people's aspirations and how they are catered to by the candidates. ## conclusion we collected a large-scale political rally speech of the 2020 presidential elections to understand how speeches and sentiments have influenced the opinion of people voting for a particular candidate. our analysis confirmed that different kinds of emotionbased sentences sway people's views about voting for a candidate. in contrast, we also observed that people wanted to listen to a particular party about a specific topic using a set of emotion-based sentences. our analysis demonstrated that if the emotion could be identified a priori and delivered by a specific candidate, the election strategy could be targeted and aligned to the voters' bias.