Publications
Selected research and technical writing across machine learning, NLP, and applied AI.
LDEB - Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues
Abstract
Emotion recognition in conversations (ERC) is vital to the advancements of conversational AI and its applications. Therefore, the development of an automated ERC model using machine learning (ML) is beneficial. However, conversational dialogues present nested emotions that entangle emotional descriptors with the emotion label. LDEB resolves this through digitization and binarization, enabling more meaningful model training. We evaluate the proposed method using hierarchical RF and ANN models on the FETA-DailyDialog dataset, demonstrating promising accuracy and precision.Citation: Dey, A., and Suthaharan, S. (2023). LDEB - Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues. arXiv:2306.02193.
Fake News Pattern Recognition using Linguistic Analysis
Abstract
This work presents a framework for detecting deception and bias in political social media content. Using linguistic normalization, feature extraction, pattern recognition, and k-nearest neighbor classification on tweets related to the 2016 election, the study demonstrates how automated linguistic analysis can support fake news detection and evaluation.Citation: A. Dey, R. Z. Rafi, S. H. Parash, S. K. Arko, and A. Chakrabarty (2018). Fake News Pattern Recognition using Linguistic Analysis. ICIEV and icIVPR.