Evaluating Compositional Approaches for Focus and Sentiment Analysis

TL;DR

This paper evaluates a compositional approach for Focus and Sentiment Analysis in NLP, demonstrating its interpretability and accuracy advantages over heuristic methods, and generalizing findings from sentiment to focus analysis.

Contribution

It introduces a formal compositional method for FA and SA using syntactic rules in Universal Dependencies, filling a research gap with quantitative evaluation and generalization.

Findings

Compositional approach outperforms heuristic methods like VADER.

The method improves interpretability and explainability in sentiment analysis.

Results generalize from sentiment to focus analysis in linguistics.

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.