Volume 2 - Issue 2, March - April 2026

This study presents a concise overview of research focused on improving customer complaint management. In modern organizations, complaints are submitted through multiple channels, including emails, chat messages, and voice calls. Existing complaint analysis systems predominantly rely on text-based sentiment analysis, which often fails to capture emotional urgency. Emotions such as anger, frustration, and stress are frequently communicated more effectively through voice intonation than through words alone. To address this, we propose a Multi-Modal Dissatisfaction Analysis System that integrates both textual semantics and voice emotion detection to assign complaint priorities with greater accuracy. The system leverages Natural Language Processing (NLP) for analyzing textual content and Speech Emotion Recognition techniques to extract emotional cues from audio complaints. Acoustic indicators such as pitch variation, energy levels, and speech intensity are combined with semantic sentiment scores generated by advanced language models. A fusion mechanism merges insights from both text and audio, enabling the system to distinguish lowrisk complaints from high-urgency ones more effectively. Experimental results demonstrate that the multimodal approach significantly outperforms text-only models in prioritizing complaints. This system provides organizations with a tool to respond promptly to critical issues, enhancing service quality and customer satisfaction. The study emphasizes the importance of incorporating emotional intelligence into modern customer support systems.

Ananya A P, Dr.M. Kayalvizhi,"Multi-Modal Dissatisfaction: Integrating Voice Tonality and Textual Semantics for High-Fidelity Complaint Priority" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(498-503) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.