Sarcasm Detection on Indonesian Twitter Feeds

Rahayu, Dwi; Kuntur, Soveatin; Hayatin, Nur

doi:10.1109/eecsi.2018.8752913

Cited by 14 publications

(3 citation statements)

References 4 publications

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“…Many of them frequently use sarcasm, i.e. positive words to convey negative thoughts, in their Twitter message as part of their creativity (Rahayu et al , 2018). Merriam Webster defined sarcasm as a method of sarcastic wit, depending on its effect on the harsh, abrasive and often ironic language which is usually directed for an individual (Dave and Desai, 2016).…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis and sarcasm detection from social network to train health-care professionals

Godara

Aron

Shabaz

2021

WJE

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Purpose Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity of social networking data, the misunderstanding, uncertainty and inaccuracy within the data have multiplied. In the textual data, the location of sarcasm is a challenging task. It is a different way of expressing sentiments, in which people write or says something different than what they actually intended to. So, the researchers are showing interest to develop various techniques for the detection of sarcasm in the texts to boost the performance of sentiment analysis. This paper aims to overview the sentiment analysis, sarcasm and related work for sarcasm detection. Further, this paper provides training to health-care professionals to make the decision on the patient’s sentiments. Design/methodology/approach This paper has compared the performance of five different classifiers – support vector machine, naïve Bayes classifier, decision tree classifier, AdaBoost classifier and K-nearest neighbour on the Twitter data set. Findings This paper has observed that naïve Bayes has performed the best having the highest accuracy of 61.18%, and decision tree performed the worst with an accuracy of 54.27%. Accuracy of AdaBoost, K-nearest neighbour and support vector machine measured were 56.13%, 54.81% and 59.55%, respectively. Originality/value This research work is original.

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Section: Introductionmentioning

confidence: 99%

Sentiment analysis and sarcasm detection from social network to train health-care professionals

Godara

Aron

Shabaz

2021

WJE

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“…Based on the accuracy achieved by individual features against the baseline model, interjection is the most common hyperbole feature used in a sarcastic tweet with an accuracy of 81.72% for RF algorithm. Past studies in sarcasm detection focusing on hyperbole mainly looked at interjection [14,20,34,40] as one of the features. In this study, the result achieved by BM 2 + HbSD Interjection as can be seen in Fig 3 is evident that interjection is indeed the main hyperbole feature for sarcasm detection and the reason why many researchers used it as part of their hyperbole approach.…”

Section: Resultsmentioning

confidence: 99%

Investigating the Importance of Hyperboles to Detect Sarcasm Using Machine Learning Techniques

Govindan,

(Corresponding Author)

2023

MJCS

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The present study aims to improve sarcasm detection mechanisms using multiple hyperboles such as interjection, intensifiers, capital letters, punctuation, and elongated words. A non-bias dataset consisting of the current pandemic related hashtags was used, namely #Chinesevirus and #Kungflu. Analysis and evaluation were performed with three distinguished machine learning algorithm that is Support Vector Machine, Random Forest and Random Forest with bagging classifiers. Each feature were analysed and the most significant hyperbole identifying sarcasm was assessed further by combining with other hyperboles. The experiments and analysis conducted using these hyperboles concluded that as a single or combined features, hyperboles enhance sarcasm especially in an unbiased dataset.

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“…From each tweet, accuracy. Rahayu et al (2018) proposed another method on Indonesian tweets based on two ext tures, namely interjection and punctuation. They also used two different weighting algorithms.…”

Section: Proposed Modelmentioning

confidence: 99%

Sarcasm Detection in Persian

Nezhad¹,

Deihimi

2020

Journal of Information and Communication Technology

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Sarcasm is a form of communication where the individual states the opposite of what is implied. Therefore, detecting a sarcastic tone is somewhat complicated due to its ambiguous nature. On the other hand, identification of sarcasm is vital to various natural language processing tasks such as sentiment analysis and text summarisation. However, research on sarcasm detection in Persian is very limited. This paper investigated the sarcasm detection technique on Persian tweets by combining deep learning-based and machine learning-based approaches. Four sets of features that cover different types of sarcasm were proposed, namely deep polarity, sentiment, part of speech, and punctuation features. These features were utilised to classify the tweets as sarcastic and nonsarcastic. In this study, the deep polarity feature was proposed by conducting a sentiment analysis using deep neural network architecture. In addition, to extract the sentiment feature, a Persian sentiment dictionary was developed, which consisted of four sentiment categories. The study also used a new Persian proverb dictionary in the preparation step to enhance the accuracy of the proposed model. The performance of the model is analysed using several standard machine learning algorithms. The results of the experiment showed that the method outperformed the baseline method and reached an accuracy of 80.82%. The study also examined the importance of each proposed feature set and evaluated its added value to the classification.

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Sarcasm Detection on Indonesian Twitter Feeds

Cited by 14 publications

References 4 publications

Sentiment analysis and sarcasm detection from social network to train health-care professionals

Sentiment analysis and sarcasm detection from social network to train health-care professionals

Investigating the Importance of Hyperboles to Detect Sarcasm Using Machine Learning Techniques

Sarcasm Detection in Persian

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