Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model

Eke, Christopher Ifeanyi; Norman, Azah Anir; Shuib, Liyana

doi:10.1109/access.2021.3068323

Cited by 79 publications

(24 citation statements)

References 49 publications

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“…e Fourth Plenary Session of the 16th CPC National People's Congress made it clear that China needs to gradually establish a comprehensive system for collecting and analyzing public opinion in order to ensure that it can further re ect public opinion. e Sixteenth Plenary Session of the 16th Central Committee reiterated its importance [3].…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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The key sentimental words in the text cannot be paid attention to effectively, and language knowledge such as the text information and the sentimental resources are relied on. Therefore, it is necessary to make full use of this unique sentimental information to achieve the best performance of the model. In order to solve the problems, a method based on the fusion of the convolutional neural network and the bidirectional GRU network text sentiment analysis capsule model to analyze the ideological and political education of public opinion is put forward. In this model, each sentiment category is combined with the attention mechanism to generate feature vectors to construct sentiment capsules. Finally, the text sentiment categories are judged according to the attributes of the capsules. The model is tested on MR, IMDB, SST-5, and the data set of the ideological and political education review. Experimental results show that compared with MC-CNN-LSTM, the readiness rate of the proposed model is improved by 5.1%, 2.8%, 2.8%, and 1.6% on four public Chinese and English data sets, respectively. Compared with LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, the accuracy of the proposed MC-BiGRU-Capsule model on MR and SST-5 data are 3.2%, 2.4%, and 3.4% higher than that of the LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, respectively. It also shows a better classification effect on multiclassification data sets. It is concluded that compared with other baseline models, this method has a better classification effect.

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Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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“…Tokenization is the initial stage in tweet preprocessing. In the tokenization process, each text data (tweet) is separated into smaller components, either sentences or words 21 . The Natural Language Toolkit (NLTk) library is used for the execution of tokenization operations.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In the tokenization process, each text data (tweet) is separated into smaller components, either sentences or words. 21 The Natural Language Toolkit (NLTk) library is used for the execution of tokenization operations. Then the non-essential information is eliminated.…”

Section: Data Preprocessingmentioning

confidence: 99%

Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based text classification

Kumar¹,

Sulochana

2022

Concurrency and Computation

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Aspect-based sentiment analysis has gained wide popularity due to its benefits of text extraction, classification, and ranking the overall sentiments of each feature extracted. However, the aspect-based feature extraction techniques often result in acquiring more number aspects that refer to the same feature which arises the need for aspect-based text classification. Since most of the existing techniques focus on monolingual aspect-based sentimental analysis, we planned to develop a multilingual aspect-based text classification for Indian languages. We perform the multilingual aspect-based text classification on different morphologically rich and complex languages such as Hindi, Tamil, Malayalam, Bengali, Urdu, Telugu, and Sinhalese. To achieve this objective, in this article we present an optimized rectified linear unit (reLU) layer-based bidirectional long short-term memory (reLU-BiLSTM) deep learning tool is developed. The parameters of the reLU-BiLSTM architecture are optimized using the local search-based five-element cycle optimization algorithm (LSFECO) optimization algorithm. Initially, the proposed model preprocesses the multilingual texts obtained from the reviews using different techniques such as tokenization, special character removal, text normalization and so forth. The discrete and categorical features from the different languages are initially extracted by applying the bidirectional encoder representations from transformers (BERT) model which processes the sentences in the text in a layer-by-layer manner. The context learning and word embeddings (aspects) present in the text are identified using different approaches such as word mover's distance, continuous Bag-of-Words (CBOW), and Cosine similarity. The LSFECO optimized reLU-BiLSTM architecture classifies the different aspects present in the embedding document to its corresponding classes (flowers, plants, animals, sports, politics, etc). The efficiency of the proposed methodology is evaluated using the text obtained from different text documents such as semantic relations from Wikipedia, Habeas Corpus (HC) Corpora, Sentiment Lexicons for 81 Languages, IIT Bombay English-Hindi Parallel Corpus, and Indic Languages Multilingual Parallel Corpus. When compared to conventional techniques, the proposed methodology outperforms them in terms of entropy, coverage, purity, processing time, accuracy, F1-score, recall, and precision.

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“…Thus, the approach may not perform well in a long text document. In a recent study, Eke, Norman [ 42 ] proposed a context-based feature technique for sarcasm identification. The author tested the predictive performance of the deep learning and BERT model on the benchmark datasets.…”

Section: Related Workmentioning

confidence: 99%

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

2021

Self Cite

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Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.

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Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model

Cited by 79 publications

References 49 publications

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based text classification

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

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