Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural
Network
- URL: http://arxiv.org/abs/2202.06776v1
- Date: Mon, 14 Feb 2022 14:55:00 GMT
- Title: Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural
Network
- Authors: Abir Chakraborty
- Abstract summary: complexity of the review sentences, presence of double negation and specific usage of words make it difficult to predict the sentiment accurately.
We propose graph Fourier transform based network with features created in the spectral domain.
Our proposed model also found competitive results on the two other recently proposed datasets from the e-commerce domain.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The objective of Aspect Based Sentiment Analysis is to capture the sentiment
of reviewers associated with different aspects. However, complexity of the
review sentences, presence of double negation and specific usage of words found
in different domains make it difficult to predict the sentiment accurately and
overall a challenging natural language understanding task. While recurrent
neural network, attention mechanism and more recently, graph attention based
models are prevalent, in this paper we propose graph Fourier transform based
network with features created in the spectral domain. While this approach has
found considerable success in the forecasting domain, it has not been explored
earlier for any natural language processing task. The method relies on creating
and learning an underlying graph from the raw data and thereby using the
adjacency matrix to shift to the graph Fourier domain. Subsequently, Fourier
transform is used to switch to the frequency (spectral) domain where new
features are created. These series of transformation proved to be extremely
efficient in learning the right representation as we have found that our model
achieves the best result on both the SemEval-2014 datasets, i.e., "Laptop" and
"Restaurants" domain. Our proposed model also found competitive results on the
two other recently proposed datasets from the e-commerce domain.
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