Textual Feature Ensemble-Based Sarcasm Detection in Twitter Data

Sundararajan, Karthik T.; Saravana, J. Vijay; Palanisamy, Anandhakumar

doi:10.1007/978-981-15-5285-4_44

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…The preprocessing stage is used as an input to the subsequent stage called the feature engineering stage Spell correction: this step is aimed at verifying the spelling of text to correct misspelled text. A common tool to correct all misspelled words is PyEnchant (the spell checker Python library) [ 6 ] Stemming: it is the restoration of extracted words to their original form or the removal of prefixes and suffixes from the word to obtain a root word called a stem. This process emphasizes on alleviating the number of keyword spaces.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Classification Approach for Sarcasm Detection

Godara

Batra

Aron

et al. 2021

Behavioural Neurology

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Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K -means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.

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Section: Introductionmentioning

confidence: 99%

“…Spell correction: this step is aimed at verifying the spelling of text to correct misspelled text. A common tool to correct all misspelled words is PyEnchant (the spell checker Python library) [ 6 ]…”

Section: Introductionmentioning

confidence: 99%

Ensemble Classification Approach for Sarcasm Detection

Godara

Batra

Aron

et al. 2021

Behavioural Neurology

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show abstract

“…The third baseline is the feature fusion method proposed in [ 85 ], which utilized the fusion of pragmatic feature, sentiment feature, and Top-200 TF-IDF features to build the context using shallow classifiers. The fourth baseline is a proposed approach studied in [ 86 ] that proposed stacking ensemble feature-based sarcasm detection in Twitter. During the evaluation experiment, the first baseline attained a promising result with a DT classifier by obtaining a precision of 0.837.…”

Section: Empirical Results and Discussionmentioning

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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“…In [19], the authors presented a feature ensemble methodology for sarcasm detection. The model incorporated various hyperbolic and pragmatic features for the purpose of identifying sarcastic statements.…”

Section: Related Workmentioning

confidence: 99%

CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media

Sivalingam¹,

Sivalingam²,

Sundararajan³

2023

Journal of Internet Technology

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<p>Text processing is an important task in various machine learning applications. One among the applications is Sentiment analysis. However, the presence of sarcasm makes it difficult for analyzing the sentiment of the statement. In the current scenario, the amount of sarcastic statements in any social media platform is high taking the forms of memes, comments, trolls etc. Hence it is important to identify sarcasm to preserve the polarity of any given statement. Sarcasm usually means the opposite of what the sentence seems to convey. While the existing works in literature have focused on detecting sarcasm, the proposed model, in addition to that, determines the levels of sarcasm present in the text, which will aid in finding the level of harshness present in the statement. In this work, an unsupervised learning model, Conditional Random Field model based Modified Expectation Maximization (CRF-MEM) algorithm has been proposed for detecting sarcasm in tweets. The proposed model aims to overcome the limitation present in the traditional EM algorithm, the random assignment factor, with the proposed aspect relationship value. Experimental results showed that the proposed CRF-MEM achieved an accuracy of 91.89% whereas the traditional EM displayed an accuracy of 80% in detecting sarcasm from text.</p> <p> </p>

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Textual Feature Ensemble-Based Sarcasm Detection in Twitter Data

Cited by 8 publications

References 8 publications

Ensemble Classification Approach for Sarcasm Detection

Ensemble Classification Approach for Sarcasm Detection

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

CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media

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