Sarcasm classification: A novel approach by using Content Based Feature Selection Method

Kumar, H. M. Keerthi; Harish, B. S.

doi:10.1016/j.procs.2018.10.409

Cited by 29 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning-based models were proposed earlier, and they primarily extract language features and train them over machine learning classifiers. Keerthi Kumar and Harish [19] used machine learning on content-based features. They utilized "mutual information" (MI), "information gain" (IG), and chi-square for feature selection methods and passed it to the clustering algorithms for further filtering.…”

Section: Related Workmentioning

confidence: 99%

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

et al. 2022

View full text Add to dashboard Cite

Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, the opinion analysis procedure is prone to errors. Concerns about the integrity of analytics have grown as the usage of automated social media analysis tools has expanded. According to preliminary research, sarcastic statements alone have significantly reduced the accuracy of automatic sentiment analysis. Sarcastic phrases also impact automatic fake news detection leading to false positives. Various individual natural language processing techniques have been proposed earlier, but each has textual context and proximity limitations. They cannot handle diverse content types. In this research paper, we propose a novel hybrid sentence embedding-based technique using an autoencoder. The framework proposes using sentence embedding from long short term memory-autoencoder, bidirectional encoder representation transformer, and universal sentence encoder. The text over images is also considered to handle multimedia content such as images and videos. The final framework is designed after the ablation study of various hybrid fusions of models. The proposed model is verified on three diverse real-world social media datasets—Self-Annotated Reddit Corpus (SARC), headlines dataset, and Twitter dataset. The accuracy of 83.92%, 90.8%, and 92.80% is achieved. The accuracy metric values are better than previous state-of-art frameworks.

show abstract

Section: Related Workmentioning

confidence: 99%

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Their experimental results showed that the lexicon-based technique performed better than the machine learning-based model. Kumar and Harish proposed a sarcastic text detection model using k-means clustering algorithms and feature selection techniques [24].…”

Section: Stop Word Removal Mentions Removalmentioning

confidence: 99%

Tanz-Indicator: A Novel Framework for Detection of Perso-Arabic-Scripted Urdu Sarcastic Opinions

Gul

Khan

Ullah

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Automatic sarcasm detection in textual data is a crucial task in sentiment analysis. This problem is complex because sarcastic comments usually carry the opposite meaning and are context-driven. The issue of sarcasm detection in comments written in Perso-Arabic-scripted Urdu text is even more challenging due to limited online linguistic resources. In this research, we proposed Tanz-Indicator, a lexicon-based framework to detect sarcasm in the user comments posted in Perso-Arabic Urdu language. We use a lexicon of over 3000 sarcastic tweets and 100 sarcastic features for experimentation. We also train two machine learning models with the same data to compare the performance of the lexicon-based model and machine learning-based model. The results show that the lexicon-based model correctly identified 48.5% sarcastic and 23.5% nonsarcastic tweets with the recall of 69.6% and 87.9% precision. The recall rate of Naïve Bayes and SVM-based machine learning models was 20.1% and 24.4%, respectively, with an overall accuracy of 65.2% and 60.1%, respectively.

show abstract

“…The efficiency of the presented method was related to another method provided to sarcasm detection shared task and sentiment analyses. Kumar and Harish [12] proposed a new method for classifying sarcastic text with content based FS technique. The projected method is composed of 2 phase FS methods for selecting better representation features.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

Kavitha¹,

Chittineni²

2022

IJACSA

View full text Add to dashboard Cite

Sarcasm is a state of speech in which the speaker says something that is externally unfriendly with a purpose of abusing/deriding the listener and/or a third person. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). Despite the fact that various methods for detecting sarcasm have been created utilizing statistical machine learning and rule-based approaches, they are unable of discerning figurative meanings of words. The models developed using deep learning approaches have shown superior performance for sarcasm detection over traditional approaches. With this motivation, this paper develops novel deep learning (DL) enabled sarcasm detection and classification (DLE-SDC) model. The DLE-SDC technique primarily involves pre-processing stage which encompasses single character removal, multispaces removal, URL removal, stop word removal, and tokenization. Next to data preprocessing, the preprocessed data is converted into the feature vector by Glove Embeddings technique. Followed by, convolutional neural network with recurrent neural network (CNN-RNN) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the CNN+RNN technique, a hyper parameter tuning process utilizing teaching and learning based optimization (TLBO) algorithm is employed in such a way that the classification performance gets increased. The DLE-SDC model is validated using the benchmark dataset and the performance is examined interms of precision, recall, accuracy, and F1-score.

show abstract

Sarcasm classification: A novel approach by using Content Based Feature Selection Method

Cited by 29 publications

References 16 publications

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

Tanz-Indicator: A Novel Framework for Detection of Perso-Arabic-Scripted Urdu Sarcastic Opinions

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

Contact Info

Product

Resources

About