Institution
Celal Bayar University
Education•Magnesia ad Sipylum, Turkey•
About: Celal Bayar University is a education organization based out in Magnesia ad Sipylum, Turkey. It is known for research contribution in the topics: Population & Heat transfer. The organization has 2960 authors who have published 6024 publications receiving 100646 citations.
Topics: Population, Heat transfer, Nanofluid, Nonlinear system, Medicine
Papers published on a yearly basis
Papers
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TL;DR: In this paper, an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks is presented. But sarcasm detection on text documents is one of the most challenging tasks in NLP.
Abstract: Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data. The purpose of our research is to present an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks. To represent text documents, we introduce inverse gravity moment based term weighted word embedding model with trigrams. In this way, critical words/terms have higher values by keeping the word-ordering information. In our model, we present a three-layer stacked bidirectional long short-term memory architecture to identify sarcastic text documents. For the evaluation task, the presented framework has been evaluated on three-sarcasm identification corpus. In the empirical analysis, three neural language models (i.e., word2vec, fastText and GloVe), two unsupervised term weighting functions (i.e., term-frequency, and TF-IDF) and eight supervised term weighting functions (i.e., odds ratio, relevance frequency, balanced distributional concentration, inverse question frequency-question frequency-inverse category frequency, short text weighting, inverse gravity moment, regularized entropy and inverse false negative-true positive-inverse category frequency) have been evaluated. For sarcasm identification task, the presented model yields promising results with a classification accuracy of 95.30%.
182 citations
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TL;DR: A Taylor method is developed to find the approximate solution of high-order linear Volterra-Fredholm integro-differential equations under the mixed conditions in terms of Taylor polynomials about any point.
181 citations
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TL;DR: A hybrid ensemble pruning scheme based on clustering and randomized search for text sentiment classification and a consensus clustering scheme is presented to deal with the instability of clustering results.
Abstract: Sentiment analysis is a critical task of extracting subjective information from online text documents. Ensemble learning can be employed to obtain more robust classification schemes. However, most approaches in the field incorporated feature engineering to build efficient sentiment classifiers.The purpose of our research is to establish an effective sentiment classification scheme by pursuing the paradigm of ensemble pruning. Ensemble pruning is a crucial method to build classifier ensembles with high predictive accuracy and efficiency. Previous studies employed exponential search, randomized search, sequential search, ranking based pruning and clustering based pruning. However, there are tradeoffs in selecting the ensemble pruning methods. In this regard, hybrid ensemble pruning schemes can be more promising.In this study, we propose a hybrid ensemble pruning scheme based on clustering and randomized search for text sentiment classification. Furthermore, a consensus clustering scheme is presented to deal with the instability of clustering results. The classifiers of the ensemble are initially clustered into groups according to their predictive characteristics. Then, two classifiers from each cluster are selected as candidate classifiers based on their pairwise diversity. The search space of candidate classifiers is explored by the elitist Pareto-based multi-objective evolutionary algorithm.For the evaluation task, the proposed scheme is tested on twelve balanced and unbalanced benchmark text classification tasks. In addition, the proposed approach is experimentally compared with three ensemble methods (AdaBoost, Bagging and Random Subspace) and three ensemble pruning algorithms (ensemble selection from libraries of models, Bagging ensemble selection and LibD3C algorithm). Results demonstrate that the consensus clustering and the elitist pareto-based multi-objective evolutionary algorithm can be effectively used in ensemble pruning. The experimental analysis with conventional ensemble methods and pruning algorithms indicates the validity and effectiveness of the proposed scheme.
181 citations
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TL;DR: The volatiles have been found out with an estimation approach by carrying out gas chromatography and mass spectrophotometer (GS-MS) Library Catalogue comparison.
180 citations
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TL;DR: In this article, the authors used a triangular wave form of conductive corrugated partition for free convection in a cavity with a corrugation partition which have different fluids on different parts of the partition was numerically examined.
178 citations
Authors
Showing all 3053 results
Name | H-index | Papers | Citations |
---|---|---|---|
Michael Berk | 116 | 1284 | 57743 |
G. Raven | 114 | 1879 | 71839 |
Tjeerd Ketel | 99 | 1067 | 46335 |
Francesco Dettori | 95 | 1026 | 41313 |
Manuel Schiller | 95 | 1004 | 41734 |
John A. McGrath | 75 | 631 | 24078 |
E. Pesen | 50 | 206 | 10958 |
Devendra Singh | 49 | 314 | 10386 |
Fatih Selimefendigil | 43 | 178 | 4522 |
Mehmet Karabacak | 40 | 111 | 3515 |
Nurullah Akkoc | 38 | 193 | 7626 |
Daiana Stolz | 38 | 239 | 7708 |
Menemşe Gümüşderelioğlu | 34 | 136 | 3328 |
Mehmet Sezer | 34 | 184 | 3543 |
Mehmet Pakdemirli | 33 | 137 | 3581 |