Different Approaches in Sarcasm Detection: A Survey

Bagate, Rupali Amit; Suguna, R.

doi:10.1007/978-3-030-34080-3_48

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…III. Dataset Datasets processed for sentiment analysis can be classified into different types depending on how many words and sentences are present [23]. Text considered for sentiment analysis or sarcasm detection is classified as short text, long text, or dialogues.…”

Section: Explainable Ai Surveymentioning

confidence: 99%

Sarcasm Detection on Text for Political Domain— An Explainable Approach

Bagate¹,

Suguna²

2022

IJRITCC

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In the era of social media, a large volume of data is generated by applications such as the industrial internet of things, IoT, Facebook, Twitter, and individual usage. Artificial intelligence and big data tools plays an important role in devising mechanisms for handling this vast volume of data as per the required usage of data to form important information from this unstructured data. When the data is publicly available on the internet and social media, it is imperative to treat the data carefully to respect the sentiments of the individuals. In this paper, the authors have attempted to solve three problems for treating the data using AI and data science tools, weighted statistical methods, and explainability of sarcastic comments. The first objective of this research study is sarcasm detection, and the next objective is to apply it to a domain-specific political Reddit dataset. Moreover, the last is to predict sarcastic words using counterfactual explainability. The textare extracted from the self-annotated Reddit corpus dataset containing 533 million comments written in English language, where 1.3 million comments are sarcastic. The sarcasm detection based model uses a weighted average approach and deep learning models to extract information and provide the required output in terms of content classification. Identifying sarcasm from a sentence is very challenging when the sentence has content that flips the polarity of positive sentiment into negative sentiment. This cumbersome task can be achieved with artificial intelligenceand machine learningalgorithms that train the machine and assist in classifying the required content from the sentences to keep the social media posts acceptable to society. There should be a mechanism to determine the extent to which the model's prediction could be relied upon. Therefore, the explination of the prediction is essential. We studied the methods and developed a model for detecting sarcasm and explaining the prediction. Therefore, the sarcasm detection model with explainability assists in identifying the sarcasmfrom the reddit post and its sentiment score to classify given textcorrectly. The F1-score of 75.75% for sarcasm and 80% for the explainability model proves the robustness of the proposed model.

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Section: Explainable Ai Surveymentioning

confidence: 99%

Sarcasm Detection on Text for Political Domain— An Explainable Approach

Bagate¹,

Suguna²

2022

IJRITCC

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“…They also executed the model against two publicly available datasets [10], [11] and seen that their model has met with a better f-score than previous systems but achieved a reduced precision value than semi-supervised sarcasm identification algorithm (SASI). Bagate and Suguna [12] have surveyed different approaches to sarcasm detection which articulated and compared various techniques for sarcasm detection. Thus, we tried to incorporate some models in our research.…”

Section: Related Workmentioning

confidence: 99%

Sarcasm detection of tweets without #sarcasm: data science approach

Bagate

Suguna²

2021

IJEECS

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Identifying sarcasm present in the text could be a challenging work. In sarcasm, a negative word can flip the polarity of a positive sentence. Sentences can be classified as sarcastic or non-sarcastic. It is easier to identify sarcasm using facial expression or tonal weight rather detecting from plain text. Thus, sarcasm detection using natural language processing is major challenge without giving away any specific context or clue such as #sarcasm present in a tweet. Therefore, research tries to solve this classification problem using various optimized models. Proposed model, analyzes whether a given tweet, is sarcastic or not without the presnece of hashtag sarcasm or any kind of specific context present in text. To achieve better results, we used different machine learning classification methodology along with deep learning embedding techniques. Our optimized model uses a stacking technique which combines the result of logistic regression and long short-term memory (LSTM) recurrent neural net feed to light gradient boosting technique which generates better result as compare to existing machine learning and neural network algorithm. The key difference of our research work is sarcasm detection done without #sarcasm which has not been much explored earlier by any researcher. The metrics used for evolutionis F1-score and confusion matrix.

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