A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach
Zeinab Rajabi, MohammadReza Valavi, Maryam Hourali

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
