Citation :

Mykola Volkivskyi, Tasriqul Islam, Stephanie Ness, Bilal Mustafa, 2024. "AI-Powered Analysis of Social Media Data to Gauge Public Sentiment on International Conflicts" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 25-33.

Abstract :

In recent years, the Internet's popularity has led to a significant rise in the spread of information through social media platforms. Given the strong interest from various sectors of society in analyzing this data, it is crucial to study improved techniques for handling and understanding this data to efficiently and accurately interpret this massive amount of information. This study examines two sentiment analysis methods to determine the population's emotions in various contexts. The first method examines the positive and negative feelings towards the 2018 presidential elections in Brazil using Twitter data. The second method involves analyzing social media data to detect threat sentiment in armed conflicts, specifically focusing on the conflict between Syria and the USA in 2017. To reach this objective, we will explore the use of AI and machine learning methods like auto-encoder and deep learning in combination with NLP text analysis techniques. The findings demonstrate that the methods used to classify sentiment in the specific domains were effective, under the methodology created for this research.

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Keywords :

Machine Learning, Deep Learning, Auto-encoder, Natural Language Processing, Sentiment Analysis, Social Media.