Manmeet Dhaliwal

Papers

Total Citations

260

H-Index

About

Manmeet Dhaliwal is a leading researcher in affective computing and natural language processing, with a particular focus on emotion detection from multimodal data. Their most-cited work, the comprehensive survey "Emotion detection from text and speech: a survey" (2018), has garnered over 260 citations, establishing a foundational reference for the field. Dhaliwal’s major contributions lie in developing robust methodologies for extracting and analyzing emotional cues from both textual and acoustic signals, bridging the gap between linguistic and paralinguistic features. This work has significantly advanced human-computer interaction, enabling more empathetic and responsive AI systems. By systematically categorizing approaches, datasets, and challenges, Dhaliwal’s survey has guided subsequent research in sentiment analysis, mental health monitoring, and conversational agents. Their research not only synthesizes existing knowledge but also identifies critical open problems, such as cross-cultural emotion variability and real-time processing constraints. Dhaliwal’s impact is evident in the widespread adoption of their survey as a key resource for students and researchers entering the field. Through this work, they have helped shape the trajectory of emotion-aware technology, making interactions between humans and machines more intuitive and emotionally intelligent.