Sentiment analysis in social network X for the evaluation of the positioning of candidates in political elections
DOI:
https://doi.org/10.51252/rcsi.v5i1.763Keywords:
KDD Methodology, Data Mining, NLP, Sentiment PolarityAbstract
Social networks are among the most important means of political communication, through which opinions are published and generated on various topics; for this reason, they are an excellent means to analyze and understand events. In this work, sentiment analysis of X/Twitter posts about the elections of presidential candidates in Mexico in 2024 was carried out via the classification of sentiment polarity to measure the positioning of the participants. A methodology based on KDD was used in this research, and 151,821 posts were analyzed about four aspiring candidates for the presidency of Mexico from the MORENA party. The results showed that the candidates best positioned in the election obtained the highest number of posts with positive polarity, although the winning candidate did not coincide with the highest percentage of positive polarities. This result indicates that it is necessary to include other variables in addition to polarity to more accurately predict the winners of political contests.
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