A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis

TL;DR

This paper introduces a Sequence Labeling Syntactic Parser (SELSP) that integrates syntactic information into sentiment analysis, significantly improving speed and accuracy over traditional parsers and heuristic methods, suitable for real-world applications.

Contribution

The paper presents a novel sequence labeling approach to dependency parsing that enhances the efficiency and accuracy of sentiment analysis pipelines.

Findings

SELSP outperforms traditional parsers like Stanza in speed and accuracy.

SELSP surpasses heuristic methods such as VADER in sentiment classification.

Using sentiment dictionaries that consider polarity variation improves prediction performance.

Abstract

Sentiment Analysis (SA) is a crucial aspect of Natural Language Processing (NLP), focusing on identifying and interpreting subjective assessments in textual content. Syntactic parsing is useful in SA as it improves accuracy and provides explainability; however, it often becomes a computational bottleneck due to slow parsing algorithms. This article proposes a solution to this bottleneck by using a Sequence Labeling Syntactic Parser (SELSP) to integrate syntactic information into SA via a rule-based sentiment analysis pipeline. By reformulating dependency parsing as a sequence labeling task, we significantly improve the efficiency of syntax-based SA. SELSP is trained and evaluated on a ternary polarity classification task, demonstrating greater speed and accuracy compared to conventional parsers like Stanza and heuristic approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER). The combination of speed and accuracy makes SELSP especially attractive for sentiment analysis applications in both academic and industrial contexts. Moreover, we compare SELSP with Transformer-based models trained on a 5-label classification task. In addition, we evaluate multiple sentiment dictionaries with SELSP to determine which yields the best performance in polarity prediction. The results show that dictionaries accounting for polarity judgment variation outperform those that ignore it. Furthermore, we show that SELSP outperforms Transformer-based models in terms of speed for polarity prediction.