Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN

Jain, Deepak Kumar; Kumar, Akshi; Garg, Geetanjali

doi:10.1016/j.asoc.2020.106198

Cited by 108 publications

(41 citation statements)

References 20 publications

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“…Then, maximum-pooling layer, drop-out layer, and finally, fully connected layer has been employed to classify text documents as either sarcastic or non-sarcastic. In another study, Kumar et al [16] presented a sarcasm identification framework based on bidirectional long short-term memory with convolution neural network.…”

Section: B Related Work On Deep Learning Based Approachesmentioning

confidence: 99%

“…Ren et al [28] presented a multi-level memory network for capturing the features of sarcasm expressions using sentiment semantics. Recently, Jain et al [16] presented a deep learning based framework for sarcasm identification in English and Hindi language. To extract semantic feature vectors, pre-trained GloVe model has been utilized.…”

Section: Volume 4 2016mentioning

confidence: 99%

“…Though term weighting and word-order information are critical on NLP tasks, the number of works dedicated to the use of term weighted word embedding schemes is very limited. In addition, stacked bidirectional long short term memory yields promising results on sentiment analysis and sarcasm identification [16] . Motivated by the use of term weighted neural language modeling in text representation to enhance predictive performance and promising performance of stacked bidirectional long short term memory, this paper seeks to introduce a sarcasm identification framework based on term weighting and stacked bidirectional LSTM.…”

Section: Introductionmentioning

confidence: 99%

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A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

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Section: B Related Work On Deep Learning Based Approachesmentioning

confidence: 99%

Section: Volume 4 2016mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

2021

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“…Consequently, attention mechanism has been widely used in natural language processing [65], statistical learning [66], and computational vision [67]. There are many variations of attention mechanism, for example soft attention [68], multi-level attention [69] and multi-dimensional attention [70], etc. More details of the category of attention mechanism can be found in [71].…”

Section: ) Attention Mechanism In Neural Networkmentioning

confidence: 99%

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Zhang

Xiang

et al. 2020

IEEE Access

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This paper reviews studies on neural networks in aerodynamic data modeling. In this paper, we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced-order models (ROMs). Subsequently, the history and fundamental methodologies of neural networks are introduced. Furthermore, we classify the neural networks based studies in aerodynamic data modeling and illustrate comparisons among them. These studies demonstrate that neural networks are effective approaches to aerodynamic data modeling. Finally, we identify three important trends for future studies in aerodynamic data modeling: a) the transformation method and physics informed models will be combined to solve high-dimensional partial differential equations; b) in the research area of steady aerodynamic response predictions, model-oriented studies and data-integration-oriented studies will become the future research directions, while in unsteady aerodynamic response predictions, radial basis function neural networks (RBFNNs) are the best tools for capturing the nonlinear characteristics of flow data, and convolutional neural networks (CNNs) are expected to replace long short-term memories (LSTMs) to capture the temporal characteristics of flow data; and c) in the field of steady or unsteady flow field reconstructions, the CNN-based conditional generative adversarial networks (cGANs) will be the best frameworks in which to discover the spatiotemporal distribution of flow field data. INDEX TERMS Aerodynamics, convolutional neural networks, neural networks, generative adversarial networks, recurrent neural networks.

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“…A lot of researches have been done lately in the domain of natural language processing. In [9] Jain, Kumar, & Garg proposed an RNN approach with Feature-rich CNN to improve the accuracy of sarcasm detection using a dataset of 3.000 sarcastic tweets and 3.000 non-sarcastic tweets in Hindi and English. This model consists of a preprocessing stage where the data is normalized by removing punctuation marks, special characters and regular expressions, then the data is stemmed to return the word to its basic form after the data is tokenized using GloVe for English and Hindi-SentiWordNet for Hindi then the result is an English vector.…”

Section: Related Workmentioning

confidence: 99%

Sarcasm Detection Using Multi-Channel Attention Based BLSTM On News Headline

Azwar

Suharjito

2020

Preprint

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Sarcasm is often used to express a negative opinion using positive or intensified positive words in social media. This intentional ambiguity makes sarcasm detection, an important task of sentiment analysis. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we introduce a multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) network to detect sarcastic headline on the news. Multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) proposed model was evaluated on the news headline dataset, and the results-compared to the CNN-LSTM and Hybrid Neural Network were excellent.

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Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN

Cited by 108 publications

References 20 publications

A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Sarcasm Detection Using Multi-Channel Attention Based BLSTM On News Headline

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