A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach

TL;DR

This paper introduces a context-based disambiguation model for sentiment concepts that leverages commonsense knowledge and semantic networks to improve polarity detection accuracy in opinionated texts.

Contribution

It presents a novel approach combining SenticNet, ConceptNet, and Numberbatch to disambiguate sentiment concepts using a bag-of-concepts method and semantic augmentation.

Findings

Achieved 82.07% accuracy on Semeval product reviews corpus.

Effectively disambiguates sentiment polarity using semantic relationships.

Enhances text representation with concept-based embeddings.

Abstract

With the widespread dissemination of user-generated content on different social networks, and online consumer systems such as Amazon, the quantity of opinionated information available on the Internet has been increased. One of the main tasks of the sentiment analysis is to detect polarity within a text. The existing polarity detection methods mainly focus on keywords and their naive frequency counts; however, they less regard the meanings and implicit dimensions of the natural concepts. Although background knowledge plays a critical role in determining the polarity of concepts, it has been disregarded in polarity detection methods. This study presents a context-based model to solve ambiguous polarity concepts using commonsense knowledge. First, a model is presented to generate a source of ambiguous sentiment concepts based on SenticNet by computing the probability distribution. Then the model uses a bag-of-concepts approach to remove ambiguities and semantic augmentation with the ConceptNet handling to overcome lost knowledge. ConceptNet is a large-scale semantic network with a large number of commonsense concepts. In this paper, the point mutual information (PMI) measure is used to select the contextual concepts having strong relationships with ambiguous concepts. The polarity of the ambiguous concepts is precisely detected using positive/negative contextual concepts and the relationship of the concepts in the semantic knowledge base. The text representation scheme is semantically enriched using Numberbatch, which is a word embedding model based on the concepts from the ConceptNet semantic network. The proposed model is evaluated by applying a corpus of product reviews, called Semeval. The experimental results revealed an accuracy rate of 82.07%, representing the effectiveness of the proposed model.