Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model

Abstract

Sarcasm emerges as a common phenomenon across social networking sites because people express their negative thoughts, hatred and opinions using positive vocabulary which makes it a challenging task to detect sarcasm. Although various studies have investigated the sarcasm detection on baseline datasets, this work is the first to detect sarcasm from a multi-domain dataset that is constructed by combining Twitter and News Headlines datasets. This study proposes a hybrid approach where the convolutional neural networks (CNN) are used for feature extraction while the long short-term memory (LSTM) is trained and tested on those features. For performance analysis, several machine learning algorithms such as random forest, support vector classifier, extra tree classifier and decision tree are used. The performance of both the proposed model and machine learning algorithms is analyzed using the term frequency-inverse document frequency, bag of words approach, and global vectors for word representations. Experimental results indicate that the proposed model surpasses the performance of the traditional machine learning algorithms with an accuracy of 91.60%. Several state-of-the-art approaches for sarcasm detection are compared with the proposed model and results suggest that the proposed model outperforms these approaches concerning the precision, recall and F1 scores. The proposed model is accurate, robust, and performs sarcasm detection on a multi-domain dataset.

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

“…Similarly, the study in [ 53 ] used an extra tree classifier (ETC) for the same task. In addition, the study in [ 54 ] used the CNN-LSTM model for sarcasm detection, and the study in [ 55 ] has performed sentiment analysis using the stacked Bi-LSTM model. For a fair comparison, these models were deployed using the COVID-19 vaccination tweets dataset that was collected in this study.…”

An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry

“…With the help of confusion matrix performance of proposed model is analysed. The performance of proposed model is analysed using accuracy, precision, recall and f-measure ( Rupapara et al, 2021 ; Jamil et al, 2021 ; Rustam et al, 2021 ). These parameters can be measured with the help of following formulae where TP represents true positives, TN is true negatives, FP shows false positives and FN shows false negatives.…”

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

“…To substantiate the performance of the proposed voting classifier, it is also compared with deep learning models. We have used three deep learning models for experiments including LSTM [ 57 ], CNN [ 58 ] and BiLSTM [ 59 ] for comparison purposes. Layered architecture and hyperparameter values are presented in Fig 7 .…”

“…To substantiate the performance of the proposed voting classifier, it is also compared with deep learning models. We have used three deep learning models for experiments including LSTM [57], CNN [58] and BiLSTM [59] Classification results of deep learning models on balanced and imbalanced datasets are presented in Table 13. It can be observed that LSTM achieves the highest result with a 0.70 value of accuracy, precision, recall, and F1 score on imbalanced data while CNN has shown the lowest results.…”