Deep Learning Approach in Complex Sentiment Analysis: A Case Study on Social Problems

This scholarly investigation examines the utilization of artificial intelligence (AI) technology in the analysis and resolution of intricate societal challenges in many countries. The originality of this study resides in the employment of deep learning algorithms, particularly Convolutional Neural Network (CNN), to execute sentiment analysis with an elevated degree of complexity. The examination encompasses three principal dimensions of sentiment: Sentiment, Tone, and Object, with the intention of offering profound insights into public perceptions regarding various social challenges. The fundamental sentiment is categorized into three classifications: Positive, Neutral, and Negative. Moreover, the Tone analysis introduces an additional layer of comprehension that encompasses Support, Suggestion, Criticism, Complaint, and Others, thereby delineating a more precise communicative context. The Object dimension is employed to ascertain the target of the sentiment, whether it pertains to an Individual, Organization, Policy, or other entity. This inquiry applied the analysis to several clusters of social issues, including Poverty and Economic Disparity, Health and Wellbeing, Education and Literacy, Violence and Security, as well as Environment and Social Life. The findings are anticipated to aid the government in devising policies that are more effective and responsive to the exigencies of society, through an enhanced understanding of public sentiment.