  The rate of terror attacks has surged over the past decade, resulting in the
tragic and senseless loss or alteration of numerous lives. Offenders behind
mass shootings, bombings, or other domestic terrorism incidents have
historically exhibited warning signs on social media before carrying out actual
incidents. However, due to inadequate and comprehensive police procedures,
authorities and social media platforms are often unable to detect these early
indicators of intent. To tackle this issue, we aim to create a multimodal model
capable of predicting sentiments simultaneously from both images (i.e., social
media photos) and text (i.e., social media posts), generating a unified
prediction. The proposed method involves segregating the image and text
components of an online post and utilizing a captioning model to generate
sentences summarizing the image's contents. Subsequently, a sentiment analyzer
evaluates this caption, or description, along with the original post's text to
determine whether the post is positive (i.e., concerning) or negative (i.e.,
benign). This undertaking represents a significant step toward implementing the
developed system in real-world scenarios.
