Sarcasmometer using sentiment analysis and topic modeling

Bhan, Namrata; D'silva, Mitchell

doi:10.1109/icac3.2017.8318782

Cited by 9 publications

(3 citation statements)

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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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“…e approach achieved 80% accuracy in sarcasm identification. Bhan and D'silva [25] suggested a system to measure sarcasm using different algorithms such as Naive Bayes, logistic regression, and linear regression where scores generated for each algorithm were compared to present the most efficient way. e linear SVC model obtained precision, recall, and F-score values of 0.86, 0.87, and 0.86, respectively.…”

Section: Literature Survey On Sarcasm Detectionmentioning

confidence: 99%

“…e polarity and sentiment score for these tweets are analyzed using TextBlob and NLTK. e change in mood 0 10 20 30 40 50 60 70 80 90 100 Accuracy (%) State-of-the-art approaches Mukherjee et al [25] Mazen et al [24] Jihen et al [21] Jayasanka et al [56] Rajadesingan et al [34] Proposed work Figure 9: Comparison of the proposed system with existing approaches. Figure 10: Sarcastic type classification based on proposed multi-rule approach.…”

Section: Modelling User's Mood Changementioning

confidence: 99%

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

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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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.

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