DSM: Document Sentiment Map

Abstract

Sentiment analysis (SA) has traditionally been treated as a static text classification task, where a single polarity label is assigned to an entire document. However, this approach fails to capture the dynamic emotional transitions and sentiment fluctuations inherent in long-form texts. This study introduces a novel process-based framework, the Document Sentiment Map (DSM), which transforms SA into a longitudinal monitoring task. The DSM model contributes two major innovations: (i) the generation of Sentiment Oscillation Charts (SOC) to visualize emotional flow and (ii) the application of Shewhart Control Chart (SCC) rules to statistically interpret sentiment patterns. To establish linguistic reliability, we determined language-specific control limits using a massive Turkish language corpus of 2.12 million sentences. We demonstrate the model’s effectiveness through case studies on diverse datasets, including news articles and children’s literature. The results show that DSM successfully identifies fine-grained emotional shifts such as dominant, distinguishable, and powerful fluctuations that are invisible to traditional classifiers. By providing an interpretable and diagnostic view of sentiment, DSM offers a systematic solution for content auditing and explainable Natural Language Processing (NLP) applications. The DSM model shifts SA from being confined to a text classification problem to a process-based perspective, focusing on the process rather than just the outcome.