Jade Obeid
Papers
1
Total Citations
2
H-Index
1
About
Jade Obeid’s research lies at the intersection of natural language processing and human-robot interaction, with a particular focus on sentiment analysis for open-domain conversational agents. In her most-cited work, “Sentiment Analysis for Open Domain Conversational Agent” (2021), Obeid investigates the applicability of common sentiment analysis models to human-robot interaction, using a dataset from the Alexa Prize–winning Alana system. Her study systematically evaluates which models are most appropriate for detecting user sentiment in real-world, unconstrained dialogue, a critical step toward more empathetic and responsive AI. Though early in her career—her top paper has garnered 2 citations—this work demonstrates a practical, data-driven approach to improving conversational agents. Obeid’s contributions are notable for bridging the gap between off-the-shelf NLP tools and the unique challenges of open-domain interaction, such as handling diverse user expressions and maintaining context. Her findings offer a foundation for future research on sentiment-aware robots, making her a promising voice in the growing field of socially intelligent AI.
Research Focus
Key Achievements
Top Papers
- 1Sentiment Analysis for Open Domain Conversational Agent2 citations · 2021