This study examines emotion classification in news headlines related to Operation Sindoor, a precision military response initiated by India in May 2025 following the Pahalgam terrorist attack. The operation’s symbolic naming, reflecting cultural mourning and national resolve, resulted in emotionally charged media coverage. To analyze the emotional framing within these headlines, a dataset from multiple news outlets was compiled and annotated with six emotion categories: anger, fear, joy, sadness, surprise, and neutral. Preprocessing involved TF‑IDF vectorization with unigram and bigram features, followed by classification using Logistic Regression and Support Vector Machines (SVM). Model performance was evaluated using accuracy, precision, recall, and F1-score, with macro-averaging to address class imbalance. Both models achieved an overall accuracy of 54%, with SVM yielding a higher macro‑F1 score (0.40) compared to Logistic Regression (0.22). The results indicate that SVM performed better in identifying dominant emotions such as fear and surprise, while both models struggled with underrepresented categories like joy and disgust. The findings suggest the need for advanced approaches such as deep learning architectures or data augmentation methods to improve classification of minority emotions in crisis reporting contexts.