# How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis

## TL;DR

This study empirically evaluates how syntactic parsing accuracy impacts rule-based sentiment analysis, finding that higher parsing accuracy does not significantly improve sentiment classification results, thus favoring faster, less accurate parsers.

## Contribution

It provides the first empirical evidence that parsing accuracy has limited effect on sentiment analysis performance, encouraging a focus on computational efficiency.

## Key findings

- All parsers yielded similar sentiment analysis results.
- Parsing accuracy did not significantly influence sentiment classification.
- Speed should be prioritized over accuracy in parser selection for sentiment analysis.

## Abstract

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.