Techniques for sentiment analysis are crucial for examining people's
opinions. People's attitudes are influenced by the constant and rapid growth in the
amount of material provided on social networks. Most research, however, focuses on
sentiment analysis to determine how a war will affect the global economy.
Consequently, when studying international conflicts, national leaders and other
influential figures tend to receive more attention than public opinion and emotions. The
purpose of the article is to discuss the analysis of moods and focus on the analysis of
emotions and social opinions during the Russian-Ukrainian conflict in order to detect
the early signs of post-traumatic stress disorder (PTSD). Post Traumatic Stress
Disorder (PTSD) and realizing Sustainable Development Goals (SDGs) are vital.
PTSD, with its impact on mental health, poses challenges to achieving Goal 3 of Good
Health and Well-being. Additionally, it hampers access to quality education (Goal 4),
especially in formative years, and can disproportionately affect women, hindering
progress towards Goal 5 of Gender Equality. In the context of conflict, PTSD impedes
the establishment of strong institutions (Goal 16) and underscores the importance of
collaborative efforts (Goal 17) to address the complex challenges associated with
trauma for a resilient and sustainable society. This study is the first to propose a model
that reflects the desire to analyze the impact of this war on mental health during the
Russian-Ukrainian armed conflict. This can protect people from mental disorders and
suicide and provide clues for future research in this area. The method used in this work
is a bidirectional LSTM method with a focus-based network approach to analyzing the
sentiment of English tweets, using positive, negative, and neutral classification in a
multiclass approach to detect signs of PTSD. Sentiment analysis is a method for
extracting emotional content from textual information using natural language
processing (NLP). Our study aims to identify people with PTSD using sentiment
analysis by training a deep learning (ML) model on text data. Based on text mood
analysis, trained models can detect post-traumatic stress disorder (PTSD). We attained a 90.02% accuracy by combining the attentional mechanism with a bidirectional shortterm memory layer (Bi-LSTM). The findings of the suggested framework demonstrate
greater accuracy than earlier state-of-the-art investigations. By creating an early
detection model, post-traumatic stress disorder (PTSD) symptoms may be lessened.
Keywords: Attention mechanism, Bidirectional LSTM-neural network, Deep neural network, Natural language processing (NLP), Post-traumatic stress disorder (PTSD), Sentiment analysis.