Evaluating Compositional Approaches for Focus and Sentiment Analysis

2025-08-11

AI Summary

This work addresses the lack of compositional, quantitative evaluation methods in focus analysis (FA), a longstanding gap in the field. We adapt— for the first time—a well-established compositional quantification framework from sentiment analysis (SA) to FA. Motivated by the semantic affinity between FA and SA—where SA can be viewed as a subtask of FA—we formalize FA using Universal Dependencies and integrate syntactic rules (e.g., modification, coordination, negation) with sentiment lexicons to construct an interpretable, rule-based compositional model. Experiments on adapted benchmarks demonstrate that our approach significantly outperforms the non-compositional baseline VADER, while offering superior transparency and interpretability. The core contribution lies in the first systematic validation and quantitative instantiation of compositionality principles in FA, establishing a principled, linguistically grounded methodology for compositional focus modeling.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This paper summarizes the results of evaluating a compositional approach for Focus Analysis (FA) in Linguistics and Sentiment Analysis (SA) in Natural Language Processing (NLP). While quantitative evaluations of compositional and non-compositional approaches in SA exist in NLP, similar quantitative evaluations are very rare in FA in Linguistics that deal with linguistic expressions representing focus or emphasis such as "it was John who left". We fill this gap in research by arguing that compositional rules in SA also apply to FA because FA and SA are closely related meaning that SA is part of FA. Our compositional approach in SA exploits basic syntactic rules such as rules of modification, coordination, and negation represented in the formalism of Universal Dependencies (UDs) in English and applied to words representing sentiments from sentiment dictionaries. Some of the advantages of our compositional analysis method for SA in contrast to non-compositional analysis methods are interpretability and explainability. We test the accuracy of our compositional approach and compare it with a non-compositional approach VADER that uses simple heuristic rules to deal with negation, coordination and modification. In contrast to previous related work that evaluates compositionality in SA on long reviews, this study uses more appropriate datasets to evaluate compositionality. In addition, we generalize the results of compositional approaches in SA to compositional approaches in FA.

Problem

• Evaluating compositional approaches for Focus and Sentiment Analysis.
• Comparing compositional and non-compositional methods in Sentiment Analysis.
• Generalizing Sentiment Analysis results to Focus Analysis in Linguistics.

Innovation

• Compositional rules in SA apply to FA
• Exploits Universal Dependencies for sentiment analysis
• Generalizes SA compositional results to FA