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

by   Carlos Gómez-Rodríguez, et al.

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.


page 1

page 2

page 3

page 4


Sparse Fuzzy Attention for Structured Sentiment Analysis

Attention scorers have achieved success in parsing tasks like semantic a...

Structured Sentiment Analysis as Dependency Graph Parsing

Structured sentiment analysis attempts to extract full opinion tuples fr...

Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa

Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarit...

Direct parsing to sentiment graphs

This paper demonstrates how a graph-based semantic parser can be applied...

Structured Sentiment Analysis as Transition-based Dependency Parsing

Structured sentiment analysis (SSA) aims to automatically extract people...

Lexicon-based Methods vs. BERT for Text Sentiment Analysis

The performance of sentiment analysis methods has greatly increased in r...

Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts

This paper summarizes the results of experimenting with Universal Depend...

Please sign up or login with your details

Forgot password? Click here to reset