Ontology-based Aspect Extraction for an Improved Sentiment Analysis in Summarization of Product Reviews

Abstract

Current approaches in aspect-based sentiment analysis ignore or neutralize unhandled issues emerging from the lexicon-based scoring (i.e., SentiWordNet), whereby lexical sentiment analysis only classifies text based on affect word presence and word count are limited to these surface features. This is coupled with considerably low detection rate among implicit concepts in the text. To address this issues, this paper proposed the use of ontology to i) enhance aspect extraction process by identifying features pertaining to implicit entities, and ii) eliminate lexicon-based sentiment scoring issues which, in turn, improve sentiment analysis and summarization accuracy. Concept-level sentiment analysis aims to go beyond word-level analysis by employing ontologies which act as a semantic knowledge base rather than the lexicon. The outcome is an Ontology-Based Product Sentiment Summarization (OBPSS) framework which outperformed other existing summarization systems in terms of aspect extraction and sentiment scoring. The improved performance is supported by the sentence-level linguistic rules applied by OBPSS in providing a more accurate sentiment analysis.