A Study on the Application of Sentiment-Support Words on Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment classification is currently an important research direction to identify the sentiment expressed by sentences in different aspects. The primary approach for performing aspect-level sentiment analysis involves extracting both grammatical and semantic information. However, analyzing the grammatical connection between aspect words and other words within a review sentence using morphological features like part of speech can be exceedingly complex. This paper proposes the concept of sentiment-supporting words, dividing sentences into aspectual words, sentiment-supporting words and non-sentiment-supporting words, which simplifies the core task of sentiment analysis. Three rules are designed for determining the “sentiment-support words” of the text in different aspects. Subsequently, the application of sentiment-support words in sentiment analysis models is given, and five classical sentiment analysis models are improved accordingly. According to the experimental outcomes on two publicly available datasets, the “sentiment-support words” and corresponding sentiment support rules proposed in this paper are capable of significantly enhancing aspect-based sentiment analysis.