A computational and statistical text analysis perspective for the Sentiment Analysis of the conversation in social media about gubernatorial election debates
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Abstract
This study examines the influence of social media on the media image and public perception of candidates in the 2023 Mexican state election debates, using computational tools and statistical analysis to assess the sentiment of 40,866 tweets. They found that Delfina Gómez of the “Juntos hacemos historia” coalition was mentioned predominantly negatively. Nevertheless, her campaign effectively communicated her media image, with the term ‘maestra’ appearing prominently in discussions about her. Conversely, Alejandra del Moral of the Va por el Estado de México coalition did not achieve a similar media impact in the tweets analysed. This research highlights the importance of social media in modern electoral campaign strategies and shows how digital discourse shapes the image construction of candidates. The integration of quantitative and qualitative analysis allows not only the quantification of mentions, but also an understanding of the context and evolution of public perception. This approach offers an alternative methodology for analysing political and social events in the digital age.
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