Abstract
The USA presidential election in 2020 has aroused the interest of society as a whole. Social media plays an essential role in campaigns due to how people express their ideas about a candidate. Therefore, analyzing messages posted on environments like Twitter is a challenging task that can be accomplished using sentiment analysis techniques. Our approach focuses on clustering algorithms that can determine positive and negative tweets related to the presidential candidates Joe Biden and Donald Trump.In addition, a new model is defined for representing a tweet, called hash index, by using the hashtag feature offered by Twitter. On the other hand, external validation is applied to the collected tweet by setting the sentiment derived from the Vader lexicon. Consequently, the experiments are evaluated using internal metrics like the Silhouette index or external ones like accuracy metrics.The experimental results show interesting achievements for the unsupervised approach that can determine a candidate’s popularity during the 2020 election.
Citare
@Inproceedings{Limboi2022AnUA,
author = {Sergiu Limboi and L. Dioşan},
booktitle = {International Symposium on INnovations in Intelligent SysTems and Applications},
title = {An unsupervised approach for Twitter Sentiment Analysis of USA 2020 Presidential Election},
year = {2022}
}