R. Deepthi, N. Divya Sruthi, Shaik Sameera, Thuremerla Lahari, Siginal Neelima, Upputuru Sushma 03 04(1) The rapid growth of the telecommunications sector has resulted in massive volumes of customeragent interaction data making manual sentiment analysis infeasible This research presents a robust framework for telecom transcript sentiment detection by combining advanced Natural Language Processing NLP techniques transformerbased embeddings and ensemble Machine Learning ML The system begins with data preprocessing including text cleaning tokenization stopword removal and lemmatization followed by exploratory analysis using word clouds document length distributions POS tagging and bigram frequency plots to uncover textual patterns Google Pathways Language Model PaLM like embeddings are then extracted using transformer models to capture rich contextual semantics and class imbalance is addressed using Random Under Sampler for uniform representation across sentiment classes Multiple ML models including Logistic Regression Classifier LRC Decision Tree Classifier DTC Extra Trees Classifier ETC Boosted Rules Classifier BRC and a custom Fast Interpretable GreedyTree Sums FIGS ensemble classifier are trained and evaluated with the FIGS model aggregating predictions from base learners to enhance accuracy robustness and generalization The framework supports realtime prediction model persistence and visualization of performance metrics providing interpretable insights for telecom operations Evaluation results including accuracy precision recall and F1score demonstrate the effectiveness of the proposed approach The system offers a scalable efficient and interpretable solution for automated sentiment detection in telecom transcripts enabling service providers to improve customer experience monitor agent performance and make datadriven operational decisions Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026