A novel automatic satire and irony detection using ensembled feature selection and data mining

Scrutinize artificial intelligence algorithms for Pakistani and Indian parody tweets detection

False information is becoming more frequent in distributing disinformation by distorting people’s awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92% .

Automatic Sarcasm Detection: Systematic Literature Review

Sarcasm is an integral part of human language and culture. Naturally, it has garnered great interest from researchers from varied fields of study, including Artificial Intelligence, especially Natural Language Processing. Automatic sarcasm detection has become an increasingly popular topic in the past decade. The research conducted in this paper presents, through a systematic literature review, the evolution of the automatic sarcasm detection task from its inception in 2010 to the present day. No such work has been conducted thus far and it is essential to establish the progress that researchers have made when tackling this task and, moving forward, what the trends are. This study finds that multi-modal approaches and transformer-based architectures have become increasingly popular in recent years. Additionally, this paper presents a critique of the work carried out so far and proposes future directions of research in the field.

WELMSD – word embedding and language model based sarcasm detection

Purpose

Sarcasm is a sentiment in which human beings convey messages with the opposite meanings to hurt someone emotionally or condemn something in a witty manner. The difference between the text's literal and its intended meaning makes it tough to identify. Mostly, researchers and practitioners only consider explicit information for text classification; however, considering implicit with explicit information will enhance the classifier's accuracy. Several sarcasm detection studies focus on syntactic, lexical or pragmatic features that are uttered using words, emoticons and exclamation marks. Discrete models, which are utilized by many existing works, require manual features that are costly to uncover.

Design/methodology/approach

In this research, word embeddings used for feature extraction are combined with context-aware language models to provide automatic feature engineering capabilities as well superior classification performance as compared to baseline models. Performance of the proposed models has been shown on three benchmark datasets over different evaluation metrics namely misclassification rate, receiver operating characteristic (ROC) curve and area under curve (AUC).

Findings

Experimental results suggest that FastText word embedding technique with BERT language model gives higher accuracy and helps to identify the sarcastic textual element correctly.

Originality/value

Sarcasm detection is a sub-task of sentiment analysis. To help in appropriate data-driven decision-making, the sentiment of the text that gets reversed due to sarcasm needs to be detected properly. In online social environments, it is critical for businesses and individuals to detect the correct sentiment polarity. This will aid in the right selling and buying of products and/or services, leading to higher sales and better market share for businesses, and meeting the quality requirements of customers.

A comparative analysis of euphemistic sentences in news using feature weight scheme and intelligent techniques

Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset.

An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.

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

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.

Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

A transformer-based approach to irony and sarcasm detection

Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164–175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.