Convolutional Neural Network Based Simile Classification System

Manjusha, P D; Raseek, C

doi:10.1109/icetietr.2018.8529117

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…Naive Bayes [43], [36], [44], [35], [45], [46], [47] SVM [40], [44], [35], [48], [49], [50], [51], [ 3) Approach: A variety of methods have been done with many varieties of techniques, including statistical models, sentiment analysis, pattern recognition, supervised or unsupervised machine learning [57]. The supervised and semi-supervised approach is used for building a model to classify data through a statistical and logical process.…”

Section: Methodsmentioning

confidence: 99%

Challenges of Sarcasm Detection for Social Network : A Literature Review

et al. 2020

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Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.

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Section: Methodsmentioning

confidence: 99%

Challenges of Sarcasm Detection for Social Network : A Literature Review

et al. 2020

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“…We observe in the literature that the classical machine learning models are used as baselines of comparison against neural networks models [17], which are modern and less feature engineering oriented approaches (as their inner architecture is capable of automatic feature extraction) [16]. However, we should not forget that the classical models can be as effective as the neural ones; the main difference is that they require more manual effort on feature engineering.…”

Section: Related Workmentioning

confidence: 99%

“…However, we should not forget that the classical models can be as effective as the neural ones; the main difference is that they require more manual effort on feature engineering. Manjusha and Raseek [16] obtained a winning 79.7 % mean F-Measure for classifying articles into satire, humor, and irony with a Convolutional Neural Network(CNN) that competed against SVM, Decision Tree, K-Nearest Neighbors(KNN), and Gaussian Naive Bayes(GNB). On the other hand, de Morais et al [20] obtained 80 % F-Measure with classical models only for a Brazilian set of Portuguese News.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Gradient - Improving Sentiment Analysis with Entropy Increase

Silva

García

Siqueira

2023

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Information sharing on the Web has also led to the rise and spread of fake news. Considering that fake information is generally written to trigger stronger feelings from the readers than simple facts, sentiment analysis has been widely used to detect fake news. Nevertheless, sarcasm, irony, and even jokes use similarwritten styles, making the distinction between fake and fact harder to catch automatically. We propose a new fake news Classifier that considers a set of language attributes and the gradient of sentiments contained in a message. Sentiment analysis approaches are based on labelling news with a unique value that shrinks the entire message to a single feeling. We take a broader view of a message’s sentiment representation, trying to unravel the gradient of sentiments a message may bring. We tested our approach using two datasets containing texts written in Portuguese: a public one and another we created with more up-to-date news scrapped from the Internet. Although we believe our approach is general, we tested for the Portuguese language. Our results show that the sentiment gradient positively impacts the fake news classification performance with statistical significance. The F-Measure reached 94 %, with our approach surpassing available ones (with a p-value less than 0.05 for our results).

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Sarcastic and Phony Contents Detection in Social Media Hindi Tweets

Sharma,

Joshi

2024

Deep Learning Techniques for Automation and Industrial Applications

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Convolutional Neural Network Based Simile Classification System

Cited by 10 publications

References 12 publications

Challenges of Sarcasm Detection for Social Network : A Literature Review

Challenges of Sarcasm Detection for Social Network : A Literature Review

Sentiment Gradient - Improving Sentiment Analysis with Entropy Increase

Sarcastic and Phony Contents Detection in Social Media Hindi Tweets

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