A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach
| Autor: | Maryam Hourali, Zeinab Rajabi, Mohammad Reza Valavi |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Word embedding Commonsense knowledge Computer science Cognitive Neuroscience 02 engineering and technology computer.software_genre Semantic network 03 medical and health sciences 0302 clinical medicine Semantic similarity Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Computer Science - Computation and Language business.industry Cosine similarity Sentiment analysis SemEval Computer Science Applications 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Computation and Language (cs.CL) 030217 neurology & neurosurgery Natural language processing |
| DOI: | 10.48550/arxiv.2008.03020 |
| Popis: | 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. Comment: This is a pre-print of an article published in Cogn Comput(2020) |
| Databáze: | OpenAIRE |
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