  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.
