Sarcasm Detection with Sentiment Semantics Enhanced Multi-level Memory Network

Ren, Lu; Xu, Bo; Lin, Hongfei; Liu, Xikai; Yang, Liang

doi:10.1016/j.neucom.2020.03.081

Cited by 63 publications

(29 citation statements)

References 12 publications

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“…The confusion matrix generated by the DLE-SDC technique on the classification of sarcasm is depicted in Fig. 5 Finally, a comprehensive comparative study of the DLE-SDC technique with other techniques takes place in Table II [27]. Fig.…”

Section: B Results Analysismentioning

confidence: 99%

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

Kavitha¹,

Chittineni²

2022

IJACSA

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Sarcasm is a state of speech in which the speaker says something that is externally unfriendly with a purpose of abusing/deriding the listener and/or a third person. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). Despite the fact that various methods for detecting sarcasm have been created utilizing statistical machine learning and rule-based approaches, they are unable of discerning figurative meanings of words. The models developed using deep learning approaches have shown superior performance for sarcasm detection over traditional approaches. With this motivation, this paper develops novel deep learning (DL) enabled sarcasm detection and classification (DLE-SDC) model. The DLE-SDC technique primarily involves pre-processing stage which encompasses single character removal, multispaces removal, URL removal, stop word removal, and tokenization. Next to data preprocessing, the preprocessed data is converted into the feature vector by Glove Embeddings technique. Followed by, convolutional neural network with recurrent neural network (CNN-RNN) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the CNN+RNN technique, a hyper parameter tuning process utilizing teaching and learning based optimization (TLBO) algorithm is employed in such a way that the classification performance gets increased. The DLE-SDC model is validated using the benchmark dataset and the performance is examined interms of precision, recall, accuracy, and F1-score.

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Section: B Results Analysismentioning

confidence: 99%

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

Kavitha¹,

Chittineni²

2022

IJACSA

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“…Taking a novel approach, Ren [181] sought to create a model to find a contrast between sentiment and situation for use in sarcasm detection. The proposed model is composed of LSTM encoders, two intra-attention memory networks and a CNN.…”

Section: Sarcasm Detection Through Lexical Analysismentioning

confidence: 99%

A Survey on Automated Sarcasm Detection on Twitter

Moores¹,

Mago²

2022

Preprint

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Automatic sarcasm detection is a growing field in computer science. Short text messages are increasingly used for communication, especially over social media platforms such as Twitter. Due to insufficient or missing context, unidentified sarcasm in these messages can invert the meaning of a statement, leading to confusion and communication failures. This paper covers a variety of current methods used for sarcasm detection, including detection by context, posting history and machine learning models. Additionally, a shift towards deep learning methods is observable, likely due to the benefit of using a model with induced instead of discrete features combined with the innovation of transformers.

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“…They used three small datasets of reviews taken from drug, car, and hotel sites. Finally, a memory network [25] was proposed using sentiment semantics to capture sarcasm expressions. They used IAC-V2, IAC-V2, and Twitter datasets for the evaluation.…”

Section: Related Workmentioning

confidence: 99%

Key Challenges and Proposed Solutions to Design Sentiment Analysis System

2022

IJIES

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Sentiment analysis (SA) is one of the most important tasks in the natural language processing (NLP) field. Many researchers have been trying to build an efficient SA system for many applications such as detection of terrorist activities, customer support management, analyzing customer feedback, market research, competitive research, and many others. Almost all these researches deal with a classification task, and they tried to solve one or two of the challenges that face this task. In this research, a complete SA system was constructed for processing five main challenges in SA and studying the effect of each one on the system. These challenges are the processing of negation, multi-polarity of words, text multi-polarity, semantically ambiguous words (exact meaning), and sarcasm. Some solutions were introduced for these challenges for getting high accuracy. It is the first work for studying the effect of these five challenges collectively with novel solutions to some of them. For aspect-based SA, three different classifiers were used; support vector machine (SVM), maximum entropy (MaxEnt), and long short-term memory (LSTM). The best results for f-measure were 0.833, 0.852, and 0.875 using the LSTM method on Foursquare ABSA, SemEval2014 (laptop and restaurant) datasets, respectively. We induced the most effective negation processing on the SA system from many test scenarios, followed by multi-polarity of words, semantically ambiguous words, text multi-polarity, and sarcasm, respectively. Also, the proposed sarcasm processing technique was evaluated on two annotated corpora with sarcasm, iSarcasm, and Rillof datasets, before applying it on SA.

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Sarcasm Detection with Sentiment Semantics Enhanced Multi-level Memory Network

Cited by 63 publications

References 12 publications

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

A Survey on Automated Sarcasm Detection on Twitter

Key Challenges and Proposed Solutions to Design Sentiment Analysis System

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