ANSEC-MM: Identifying Antecedents of Negative Public Sentiment Through Expression Capacity: A Mixed-Methods Approach to Crisis Mitigation

Social networks have emerged as integral platforms for communication and information dissemination in contemporary society. The spread of negative sentiments and its impact on activities of users in social networks is a crucial issue. When users receive negative reviews about news or articles, regardless of authenticity, they form opinions based on their own understanding, and statistics show that more than 90% of the time this reveals predictable behavior patterns. To address this situation, the proposed Antecedents of Negative Sentiment through Expression Capacity: Mixed Methods (ANSEC-MM) study identifies the antecedents of negative sentiment using expression capacity as a mixed-methods approach to mitigate the generation of negative sentiments. The proposed model introduces the concept of identification of influencer nodes with further categorization into active and inactive influencer nodes. The model separates negative influencer nodes from positive nodes and processes the negative influencer nodes further. A Node Expressive Capacity (NE) metric predicts the frequency with which users interact with neighboring influencer nodes, which contributes to the generation of negative sentiments. A Cognitive Effect Coefficient (φ) defines the temperament status of the users. Through further computation, the model distinguishes the proportion of negative sentiments from positive ones. Negative sentiment mitigation is achieved through a developed algorithmic approach. Performance is tested and compared across three datasets against state-of-the-art models: EANN, BERT, and AOAN. The proposed model demonstrated superior performance in negative sentiment detection and mitigation, achieving accuracy rates of 90% and 88%, respectively, compared to existing models.