TY - CHAP
T1 - Sentiment Analysis of a Social Network to Detect Political Propaganda
AU - Abril Cabrera, Juliana Nicole
AU - Granda Salamea, Camila Verónica
AU - Muñoz Tufiño, Steven Marcelo
AU - Veintimilla Reyes, Jaime Eduardo
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026/2/8
Y1 - 2026/2/8
N2 - This paper focuses on the development of a sentiment analysis model to detect political propaganda using a convergent neural network, specifically BERT (Bidirectional Encoder Representations from Transformers). Available data in the form of comments are preprocessed and tokenized to train the model effectively. The model is then built and fine-tuned using these comments to classify sentiments into positive or negative categories. The effectiveness of the model is evaluated through practical experiments, and the results obtained are examined. The main focus is on understanding how this technique can help accurately identify and analyze political propaganda, which can have a significant impact on political campaigns and public opinion analysis. By utilizing advanced machine learning techniques, this paper aims to contribute to the growing field of natural language processing and sentiment analysis, providing insights that can aid in the detection and understanding of political messaging and its influence on the public. This research underscores the importance of developing sophisticated tools to parse and interpret the vast amounts of data generated by social media and other online platforms, ultimately aiming to foster a more informed and discerning public discourse.
AB - This paper focuses on the development of a sentiment analysis model to detect political propaganda using a convergent neural network, specifically BERT (Bidirectional Encoder Representations from Transformers). Available data in the form of comments are preprocessed and tokenized to train the model effectively. The model is then built and fine-tuned using these comments to classify sentiments into positive or negative categories. The effectiveness of the model is evaluated through practical experiments, and the results obtained are examined. The main focus is on understanding how this technique can help accurately identify and analyze political propaganda, which can have a significant impact on political campaigns and public opinion analysis. By utilizing advanced machine learning techniques, this paper aims to contribute to the growing field of natural language processing and sentiment analysis, providing insights that can aid in the detection and understanding of political messaging and its influence on the public. This research underscores the importance of developing sophisticated tools to parse and interpret the vast amounts of data generated by social media and other online platforms, ultimately aiming to foster a more informed and discerning public discourse.
KW - BERT
KW - Convergent neural network
KW - Political propaganda
KW - Sentiment analysis
KW - BERT
KW - Convergent neural network
KW - Political propaganda
KW - Sentiment analysis
UR - https://www.scopus.com/pages/publications/105030259814
UR - https://link.springer.com/chapter/10.1007/978-3-031-98768-7_4
U2 - 10.1007/978-3-031-98768-7_4
DO - 10.1007/978-3-031-98768-7_4
M3 - Capítulo
AN - SCOPUS:105030259814
SN - 978-3-031-98767-0
VL - 1
T3 - Lecture Notes in Networks and Systems
SP - 53
EP - 65
BT - Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2024
A2 - García, Marcelo V.
A2 - Reyes, John Paul
A2 - Núñez, Carlos
A2 - Gordón Gallegos, Carlos
PB - Springer Nature
T2 - 6th International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2024
Y2 - 21 October 2024 through 25 October 2024
ER -