A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification

Onan, Aytuğ; Korukolu, Serdar; Bulut, Hasan

doi:10.1016/j.ipm.2017.02.008

Cited by 281 publications

(147 citation statements)

References 51 publications

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“…On other hand, work of [37] [38] emphasizes that individual prediction of various classifiers can be ensemble so that a more robust and accurate classification model can be built. The ensemble learning plays a vital role in recent research activity of pattern recognition and machine learning [38].…”

Section: F Ensemble Classifiersmentioning

confidence: 99%

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Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Kumar¹,

Harish²

2019

IJIMAI

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With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods.

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Section: F Ensemble Classifiersmentioning

confidence: 99%

“…The automatic text polarity identification process is known as Sentiment Analysis (SA) or Opinion Mining. Sentiment Analysis (SA) aims to classify a given text into positive, negative or neutral polarity [4] [53]. There are many challenges related to SA which need to be addressed and resolve.…”

Section: Introductionmentioning

confidence: 99%

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Kumar¹,

Harish²

2019

IJIMAI

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“…PSO is an efficient optimization algorithm, but the PSO algorithm can easily fall into the local optimum and undergo premature convergence in the global search process. Additionally, the effect of random oscillation is slowed down during the later stage of convergence [38,39]. Taking this information into account, we present SA algorithm to optimize PSO, which can overcome the drawbacks of PSO algorithm and help the particle jump out of the local optimal and converge to global optimal solution.…”

Section: Sapso Algorithmmentioning

confidence: 99%

Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis

Liu

Shen

et al. 2018

Mathematical Problems in Engineering

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As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.

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“…Studies considering the use of evolutionary algorithms for optimizing the polarity values of opinion concepts have been proposed only recently (Ferreira et al, 2015;Onan et al, 2016Onan et al, , 2017. However, these works focused on learning candidate refinements of opinion concepts polarity without considering the context dimension associated with them.…”

Section: Related Workmentioning

confidence: 99%

NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text

Dragoni

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes the NeuroSent system that participated in SemEval 2018 Task 1. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.

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A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification

Cited by 281 publications

References 51 publications

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis

NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text

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