Researchers create multimodal sentiment analysis method that improves detection of human emotions while reducing computational cost
Peer-Reviewed Publication
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 31-Dec-2025 02:11 ET (31-Dec-2025 07:11 GMT/UTC)
Multimodal sentiment analysis is an information processing technique that attempts to predict human emotional states from multiple modalities like text, audio, and video. Due to challenges in aligning multiple modalities, existing methods are limited to analysis at course or fine granularity, which risks missing nuances in human emotional expression. Researchers have now developed an innovative approach to MSA that reduces computational time required to sentiment prediction while offering improved performance.
On Tuesday, August 12, 2025, the University of Pittsburgh Cyber Energy Center and Pitt Cyber hosted “Transforming Cybersecurity: A Multidisciplinary Approach to Risk, Technology, and Policy.” The in-person, day-long workshop brought together experts from across industries and disciplines to assess the current state of cybersecurity through a multidisciplinary lens.
Families, particularly those already vulnerable to food insecurity, can face difficulties obtaining food in the aftermath of natural disasters. University of Houston researchers will utilize artificial intelligence to develop an online resource for food pantries, aiming to streamline stakeholder collaboration and distribute resources to families in need.
Researchers from the Urban Resilience AI Lab at Texas A&M University have used machine learning to create a nationwide Power System Vulnerability Index (PSVI) that identifies areas at increased risk of power outages.
The SETI Institute awarded the Davie Postdoctoral Fellowship for AI/ML-driven exoplanet discovery to Isabel Angelo. Machine learning is changing the way we search for exoplanets and making it possible to discover patterns in massive datasets. Angelo’s research will refine and expand ML-driven pipelines for detecting exoplanets, and she will work with SETI Institute researcher Dr. Vishal Gajjar and his team and collaborators at the SETI Institute and IIT Tirupati in India.
The project will enhance supervised Convolutional Neural Network (CNN) architectures and integrate anomaly-detection techniques to identify subtle or unconventional exoplanet candidates hidden in massive datasets. These could include ringed or disintegrating worlds, exocomets, complex multi-planet systems and possibly signs of alien megastructures.