Abstract: Now-a-days, using the social network Twitter, a subject can easily access, post, and share information about news, events, and incidents taking place currently in the world. Recently, due to the high number of users and the capability to transfer information instantly, Twitter had attracted the interest of politicians with the goal to interact with their followers and to communicate theirs polices. Fearing the disagreements and disturbances that the application of some policies might cause, usually politicians use surveys to support their actions. However, such studies still use traditional questionnaires to recover information and are costly and time-consuming. Recent advances in automatic natural language processing have allowed the extraction of information from textual data, like tweets. In this work, we present a method to analyze Twitter data related to Peruvian politicians and able to score the latent sentiment polarity of such messages. Our proposal is based on an embedding representation of tweets, which are classified by a convolutional neural network. For evaluation, we collected a new dataset related to the current President of Peru, where the model achieved 91.2%of sensibility and 94.4% of specificity. Furthermore, we evaluated the model in two politic topics, that were totally unknown for the model. In all of them, our approach gives comparable results to renowned Peruvian pollsters.

Jose Yauri, Luis Solis, Efrain Porras, Manuel Lagos and Enrique Tinoco, “Approval Rating of Peruvian Politicians and Policies using Sentiment Analysis on Twitter” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130696

@article{Yauri2022,
title = {Approval Rating of Peruvian Politicians and Policies using Sentiment Analysis on Twitter},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130696},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130696},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Jose Yauri and Luis Solis and Efrain Porras and Manuel Lagos and Enrique Tinoco}
}