Sparse Representation for Sentiment Analysis

Karimi, Zohre; Ramezani, Rohollah

doi:10.1109/icspis51611.2020.9349609

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main contribution of this paper is applying feature extraction on sentiment data by considering laying sentiment data on some manifolds and discovering manifolds' structure by both local information and sparse properties of data. It is noteworthy to highlight the main aspects of this research in comparison to our previous research [21]:…”

mentioning

confidence: 94%

Manifold-based Sparse Representation for Opinion Mining

Karimi

2023

Preprint

Self Cite

View full text Add to dashboard Cite

This paper focuses on how to enhance feature representation for opinion mining. The classical feature representation methods suffer from high dimensionality, sparsity , noisy, irrelevant and redundant information. It is proposed to exploit the manifold assumption and sparse property as prior knowledge for opinion representation to learn effective features. First, the graph representation of user reviews based on the mentioned prior knowledge is learned. Then, the spectral properties of the learned graph are exploited to present data in a new feature space. The proposed algorithm is applied to four various common input features on two benchmark datasets, Internet Movie Database (IMDB) and Amazon review dataset. Our experiments reveal that the proposed algorithm yields considerable enhancements in terms of F-measure, accuracy, and other standard performance measures compared to the combination of state-of-the-art features with various classifiers. The highest classification accuracies of 99.15 and 91.97 are obtained in the proposed method on IMDB and Amazon exploiting linear SVM classifier, respectively. The impact of parameters of the proposed algorithm is also investigated in this paper.

show abstract

mentioning

confidence: 94%

Manifold-based Sparse Representation for Opinion Mining

Karimi

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Manifold-based sparse representation for opinion mining

Karimi

2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

What the consumer thinks about an organization's products, services, and events is a crucial performance indicator for businesses. The brief opinion pieces were quickly published on websites and social media platforms and have been analyzed by machine learning methods. The classical text feature representation methods suffer from high dimensionality, sparsity, noisy, irrelevant and redundant information. This paper focuses on how to enhance feature representation for opinion mining. Some nonlinear feature selection methods based on manifold assumption have been exploited to resolve these problems. The inherent manifold configuration was commonly ascertained through a nearest neighbor graph, whereby the neighbors in the current techniques may exhibit diverse polarities. To alleviate this burden, it is proposed to exploit both manifold assumption and sparse property as prior knowledge for opinion representation to learn intrinsic structure from data. First, the graph representation of user reviews based on the mentioned prior knowledge is learned. Then, the spectral properties of the learned graph are exploited to present data in a new feature space. The proposed algorithm is applied to four various common input features on two benchmark datasets, the Internet Movie Database (IMDB) and the Amazon review dataset. Our experiments reveal that the proposed algorithm yields considerable enhancements in terms of F-measure, accuracy, and other standard performance measures compared to the combination of state-of-the-art features with various classifiers. The highest classification accuracies of 99.15 and 91.97 are obtained in the proposed method on IMDB and Amazon using a linear SVM classifier, respectively. The impact of the parameters of the proposed algorithm is also investigated in this paper. The incorporation of a sparse manifold-based representation has led to noteworthy advancements beyond the baseline, and this success serves to validate the underlying assumptions.

show abstract

Sparse Representation for Sentiment Analysis

Cited by 2 publications

References 16 publications

Manifold-based Sparse Representation for Opinion Mining

Manifold-based Sparse Representation for Opinion Mining

Manifold-based sparse representation for opinion mining

Contact Info

Product

Resources

About