Decoding Complexity and Emotion: A Computational Linguistic Approach to Sentence Comprehensibility and Writer Affect

Grasping the cognitive and emotional foundations of written language is vital for developing AI systems that better align with human needs and for progressing language understanding technologies. This study examines how both the complexity of sentences and the emotional state of the writer can be modeled computationally using a comprehensive, multimodal strategy. In particular, we investigate the comprehensibility of sentences through Cognitive Load Prediction Models that are trained on eye-tracking and EEG-annotated datasets, utilizing the Zurich Cognitive Language Processing Corpus (ZuCo) to anchor complexity analysis in actual human cognitive signals. At the same time, to assess the writer’s emotional state, we present a neuro-symbolic NLP framework that merges rule-based sentiment techniques (such as negation patterns and emotion lexicons) with deep neural representations to enhance emotional detail and interoperability. Additionally, we include multimodal behavioral indicators like typing speed, keystroke dynamics, and real-time writing hesitations to map cognitive and emotional trends during the writing process. Our proposed architecture integrates these modalities—textual, physiological, and behavioral—to develop robust models that can predict the processing difficulty of sentences and the underlying emotions of the writer. Experimental findingsindicate that combining symbolic reasoning with contextual embeddings, supplemented by physiological and behavioral information, significantly boosts prediction accuracy compared to models relying solely on text. This research establishes a groundwork for sophisticated linguistic intelligence systems that can interpret not just the content of writing, but also the motivations behind it.