Papers
1
Total Citations
5
H-Index
1
About
Chandni Magoo is a researcher at the forefront of natural language processing and intent detection, with a focus on extracting meaningful insights from social media data. Her most cited work, "A Novel Hybrid Approach for Intent Creation and Detection Using K-Means-Based Topic Clustering and Heuristic-Based Capsule Network" (2022, 5 citations), introduces an innovative methodology that combines unsupervised topic clustering with advanced capsule network architectures. This hybrid approach addresses the critical challenge of identifying user intents—such as opinions, complaints, or inquiries—from noisy, unstructured user reviews. By leveraging K-means clustering to group related topics and a heuristic-based capsule network for precise classification, Magoo’s contribution enhances the accuracy and efficiency of intent detection systems, which are vital for businesses and policymakers monitoring public sentiment. Her work has garnered attention for its practical applicability in social media analytics, offering a scalable solution for real-time intent recognition. Magoo’s research bridges the gap between traditional clustering techniques and deep learning, paving the way for more robust and interpretable models in computational linguistics. Her efforts underscore the growing importance of automated intent analysis in understanding online communities.
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Top Papers
- 1