Chandrakant Bothe
Papers
2
Total Citations
15
H-Index
2
About
Chandrakant Bothe is a researcher at the intersection of natural language processing, sentiment analysis, and human-robot interaction. His work focuses on enabling machines to understand and respond to human emotions and social cues in dialogue. Bothe’s most-cited paper, "Dialogue-Based Neural Learning to Estimate the Sentiment of a Next Upcoming Utterance" (2017, 12 citations), introduces a novel neural approach for predicting the emotional trajectory of a conversation before it unfolds—a key capability for adaptive, empathetic AI. He further explores socially aware robotics in "Towards Dialogue-Based Navigation with Multivariate Adaptation Driven by Intention and Politeness for Social Robots" (2018, 3 citations), where he models how robots can adjust their behavior based on a user’s intent and politeness level. Though his citation counts are modest, Bothe’s contributions are foundational for creating more natural, context-sensitive interactions between humans and machines. His work is particularly relevant for students and researchers interested in affective computing, dialogue systems, and socially assistive robotics.
Research Focus
Key Achievements
Top Papers
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