Sarcasm Detection Approaches for English Language

Katyayan, Pragya; Joshi, Nisheeth

doi:10.1007/978-3-030-03131-2_9

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…For instance, "your good friend is present". • Illocutionary Sarcasm: It contains quasi cues that suggest an attitude that is contrary to what is actually said (Katyayan & Joshi, 2019).…”

Section: Sarcasm Typesmentioning

confidence: 99%

“…The six-tupled examples of sarcasm are: < S, H, C, U, P, P > Where, S = Speaker, H = Hearer or Listener, C = Context, U = Utterances, P = Literal Proposition, P = Intended Proposition. In essence, Speaker S produces an utterance U in context C that refers to proposition P while the hearer H intends to comprehend the proposition (Katyayan & Joshi, 2019). Figure 1 shows the textonym of sarcasm text detection.…”

Section: Introductionmentioning

confidence: 99%

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Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Singh

2024

ADCAIJ

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One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.

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“…For instance, "your good friend is present". • Illocutionary Sarcasm: It contains quasi cues that suggest an attitude that is contrary to what is actually said (Katyayan & Joshi, 2019).…”

Section: Sarcasm Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Singh

2024

ADCAIJ

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“…Arabian, Hebrew) (Kincl, Novák & Přibil, 2019). The identification of irony, sarcasm and humour also still presents a big challenge for scholars in the field (Katyayan & Joshi, 2019;Farias & Rosso, 2017).…”

Section: Issues Controversies Problemsmentioning

confidence: 99%

Unraveling E-WOM Patterns Using Text Mining and Sentiment Analysis

Guerreiro¹,

Loureiro²

2022

Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines

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Electronic word-of-mouth (e-WOM) is a very important way for firms to measure the pulse of its online reputation. Today, consumers use e-WOM as a way to interact with companies and share not only their satisfaction with the experience, but also their discontent. E-WOM is even a good way for companies to co-create better experiences that meet consumer needs. However, not many companies are using such unstructured information as a valuable resource to help in decision making: first, because e-WOM is mainly textual information that needs special data treatment and second, because it is spread in many different platforms and occurs in near-real-time, which makes it hard to handle. The current chapter revises the main methodologies used successfully to unravel hidden patterns in e-WOM in order to help decision makers to use such information to better align their companies with the consumer's needs.

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“…This approach showed very promising results over traditional approaches. Sharma and Joshi [45] developed a rule-based word sense disambiguation approach for Hindi. It gave an accuracy of 73%.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of English-Hindi Interactive Machine Translation

Sharma¹,

Seth²,

Syed³

et al. 2019

IJEAT

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Machine Translation systems are still far from being perfect and to improve their performance the concept of Interactive Machine Translation (IMT) was introduced. This paper proposes an IMT system, which uses Statistical Machine Translation and a bilingual corpus on which several algorithms (Word error rate, Position Independent Error Rate, Translation Error Rate, n-grams) are implemented to translate text from English to Indian languages. The proposed system improves both the speed and productivity of the human translators as found through experiments.

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Sarcasm Detection Approaches for English Language

Cited by 11 publications

References 18 publications

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Unraveling E-WOM Patterns Using Text Mining and Sentiment Analysis

Development of English-Hindi Interactive Machine Translation

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