Sarcasm identification in textual data: systematic review, research challenges and open directions

Eke, Christopher Ifeanyi; Norman, Azah Anir; Shuib, Liyana; Nweke, Henry Friday

doi:10.1007/s10462-019-09791-8

Cited by 55 publications

(29 citation statements)

References 69 publications

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“…Despite this, Mahlawi and Sasi [77] found that, from the large number of Enron email datasets, data are extracted by using NLP and sentiment analysis to make them available in a structured format. Furthermore, the authors Eke, Norman, Shuib, and Nweke [78] noted that the other parts of NLP are also important. In that, lexical analysis and ML-based emotional behavior detected from the text messages were used to check the level of criticism or hurt level from the Sarcasm dataset.…”

Section: Study Reference Domain Contributionsmentioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

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Section: Study Reference Domain Contributionsmentioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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“…The summary of the related work is depicted in Table 2 . A detailed analysis of the feature engineering techniques employed in sarcasm classification tasks can be found in a systematic literature review conducted in [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

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

2021

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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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“…This study would similarly utilise Reddit to develop a novel corpus of sarcastic utterances for the purpose of training an algorithm to detect sarcasm. It appears that in the vast majority of cases, researchers use either an existing corpus of sarcastic utterances (Ghosh et al, 2018;Ghosh et al, 2020) such as the one compiled by Khodak et al (2018), or more commonly according to Eke et al (2019), they gather their own unique data to use just in their study (Avvaru et al, 2020;Ghosh et al, 2020;Ren et al, 2018). These corpora generally tend to be considerably smaller (e.g.…”

Section: Data Collectionmentioning

confidence: 99%