Trie Maya Kadarina

About

Trie Maya Kadarina is a researcher whose work bridges natural language processing and robotics, with a particular focus on sentiment classification and human-robot interaction. Her most cited paper, "Sentiment classification of delta robot trajectory control using word embedding and convolutional neural network" (2022, 3 citations), introduces an innovative approach that applies sentiment analysis—traditionally used for extracting emotions and opinions from text—to control the trajectory of a delta robot. By integrating word embedding techniques with convolutional neural networks, Kadarina demonstrates how subjective information from unstructured text can be translated into actionable commands for robotic systems, enhancing the responsiveness and intuitiveness of human-robot collaboration. This work highlights her contribution to making robots more adaptive to human emotional cues, a key challenge in interactive automation. While her citation count is modest, the interdisciplinary nature of her research—combining NLP, deep learning, and robotics—positions her as a pioneer in sentiment-driven robotic control. Kadarina’s work holds promise for applications in assistive robotics, manufacturing, and service industries, where understanding human intent through language can improve safety and efficiency. Her research underscores the growing potential of affective computing in real-world robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Sentiment classification of delta robot trajectory control using word embedding and convolutional neural network

   3 citations · 2022

Key Collaborators

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